US 20020191034 A1 Resumen A system for performing visible object determination. Visualization software running on a host processor represents space with a hierarchy of cones, and generates a hierarchy of bounding hulls from a collection of objects by recursively grouping clusters of objects. Each hull node in the hull hierarchy stores parameters which characterize a bounding hull for the corresponding cluster or object. The visualization software searches the cone and hull hierarchies starting with the root cone and the root hull. Before exploring a given cone-hull pair, a normalized cone size for the cone and a normalized hull size for the hull may be computed, and the sizes compared. If the cone size is larger than the hull size, subcones of the cone are explored with respect to the hull. Conversely, if the hull size is larger than the cone size, subhulls of the hull are explored with respect to the cone.
Reclamaciones(50) 1. A method for displaying visible objects on a display device, the method comprising:
searching a cone tree structure and a bound tree structure to determine one or more nearest graphical objects for one or more cones of the cone tree structure, wherein, for a first cone of the cone tree structure and a first bound of the bound tree structure, said searching the cone tree structure and the bound tree structure includes:
computing a bound size for the first bound;
comparing the bound size to a cone size corresponding to the first cone;
searching subbounds of the first bound with respect to the first cone if the bound size is larger than the cone size;
transmitting an indication of the one or more nearest graphical objects for each of the one or more cones for rendering. 2. The method of 3. The method of computing a first cone-bound separation value for a first subbound of the first bound with respect to the first cone; determining whether the first cone-bound separation value satisfies an inequality condition with respect to a measurement value associated with the first cone; searching said first subbound with respect to said first cone in response to determining that the first cone-bound separation value satisfies the inequality condition with respect to the measurement value associated with the first cone. 4. The method of searching subcones of the first cone with respect to the first bound if the bound size is smaller than the cone size. 5. The method of computing a first cone-bound separation value for a first subcone of the first cone with respect to the first bound; determining whether the first cone-bound separation value satisfies an inequality condition with respect to a measurement value associated with the first subcone; searching the first subcone with respect to the first bound in response to the first cone-bound separation value satisfying the inequality condition with respect to the measurement value associated with the first subcone. 6. The method of 7. The method of 8. The method of (a) determining if said second cone is a leaf of the cone tree structure and said second bound is a leaf bound of the bound tree structure; (b) setting a measurement value associated with the second cone equal to an extent of separation between the second bound and the second cone; (c) setting a visible object attribute associated with the second cone equal to an object pointer value corresponding to the second bound; wherein (b) and (c) are performed in response to said second cone being a leaf of the cone tree structure and said second bound being a leaf of the bound tree structure. 9. The method of computing the cone size for the first cone based on cone parameters associated with the first cone stored in the cone tree structure. 10. The method of reading the cone size corresponding to the first cone from a memory. 11. The method of receiving a cone pointer which points to the cone tree structure stored in a memory; and receiving a bound pointer which points to a bound tree structure stored in the memory, wherein leaf bounds of the bound tree structure approximate a collection of graphical objects. 12. A computer system for displaying visible objects on a display device, the computer system comprising:
a memory configured to store a cone tree structure and a bound tree structure, wherein leaf bounds of the bound tree structure approximate a collection of graphical objects; a processor configured to execute a visibility search algorithm stored in the memory; wherein, in response to an execution of the visibility search algorithm, the processor is configured to search the cone tree structure and the bound tree structure to determine one or more nearest graphical objects for one or more cones of the cone tree structure, wherein, for a first cone of the cone tree structure and a first bound of the bound tree structure, said processor is configured to (a) compute a bound size for the first bound, (b) compare the bound size to a cone size corresponding to the first cone, (c) search subbounds of the first bound with respect to the first cone if the bound size is larger than the cone size, wherein the processor is further configured to transmit an indication of the one or more nearest graphical objects for each of the one or more cones for rendering. 13. The computer system of 14. The computer system of computing a first cone-bound separation value for a first subbound of the first bound with respect to the first cone; determining whether the first cone-bound separation value satisfies an inequality condition with respect to a measurement value associated with the first cone; searching the first subbound with respect to the first cone in response to determining that the first cone-bound separation value satisfies the inequality condition with respect to the measurement value associated with the first cone. 15. The computer system of 16. The computer system of computing a first cone-bound separation value for a first subcone of the first cone with respect to the first bound; determining whether the first cone-bound separation value satisfies an inequality condition with respect to a measurement value associated with the first subcone; and searching said first subcone with respect to said first bound in response to the first cone-bound separation value satisfying the inequality condition with respect to the measurement value associated with the first subcone. 17. The computer system of 18. The computer system of (i) determining if said second cone is a leaf of the cone tree structure and said second bound is a leaf bound of the bound tree structure; (ii) setting the measurement value associated with the second cone equal to a second measurement value of separation between the second bound and the second cone; (iii) setting a visible object attribute associated with the second cone equal to an object pointer value corresponding to the second bound; wherein said processor is configured to perform (ii) and (iii) in response to said second cone being a leaf of the cone tree structure and said second bound being a leaf of the bound tree structure. 19. The computer system of 20. The computer system of 21. A computer system for displaying visible objects on a display device, the computer system comprising:
a memory configured to store a cone hierarchy and a bounding hull hierarchy, wherein terminal bounds of the bounding hull hierarchy approximate a collection of graphical objects; a processor configured to execute a visibility search algorithm stored in the memory; wherein, in response to an execution of the visibility search algorithm, the processor is configured to search the cone hierarchy and the bounding hull hierarchy to determine one or more nearest graphical objects for one or more cones of the cone hierarchy, wherein said processor is configured to (a) compute a hull size for a first hull of the bounding hull hierarchy, (b) compare the hull size to a cone size corresponding to a first cone of the cone hierarchy, and (c) search subhulls of the first hull with respect to the first cone if the hull size is larger than the cone size, wherein the processor is further configured to transmit an indication of the one or more nearest graphical objects for the one or more cones for rendering and display. 22. The computer system of 23. The computer system of 24. A computer-readable medium comprising a plurality of computer program instructions, wherein the plurality of computer program instructions are executable by one or more processors to perform:
searching a cone tree structure and a bound tree structure to determine one or more visible objects for one or more cones of the cone tree structure, wherein, said searching the cone tree structure and the bound tree structure comprises searching a first cone of the cone tree structure with respect to a first bound of the bound tree structure, wherein said searching the first cone with respect to the first bound comprises:
estimating a normalized bound size for the first bound;
comparing the normalized bound size to a normalized cone size corresponding to the first cone;
searching subbounds of the first bound with respect to the first cone if the normalized bound size is larger than the normalized cone size;
transmitting an indication of the one or more visible objects for each of the one or more cones for rendering and display. 25. The computer-readable medium of 26. The computer-readable medium of computing a first cone-bound separation value for a first subbound of the first bound with respect to the first cone; determining whether the first cone-bound separation value satisfies an inequality condition with respect to a measurement value associated with the first cone; searching said first subbound with respect to said first cone in response to determining that the first cone-bound separation value satisfies the inequality condition with respect to the measurement value associated with the first cone. 27. The computer-readable medium of searching subcones of the first cone with respect to the first bound if the normalized bound size is smaller than the normalized cone size. 28. The computer-readable medium of computing a first cone-bound separation value for a first subcone of the first cone with respect to the first bound; determining whether the first cone-bound separation value satisfies an inequality condition with respect to a measurement value associated with the first subcone; searching the first subcone with respect to the first bound in response to the first cone-bound separation value satisfying the inequality condition with respect to the measurement value associated with the first subcone. 29. The computer-readable medium of setting the measurement value associated with the first cone equal to an extremum of measurement values associated with said subcones after said searching said subcones with respect to the first bound. 30. The computer-readable medium of 31. The computer-readable medium of (a) determining if said second cone is a leaf of the cone tree structure and said second bound is a leaf bound of the bound tree structure; (b) setting a measurement value associated with the second cone equal to an extent of separation between the second bound and the second cone; (c) setting a visible object attribute associated with the second cone equal to an object pointer value corresponding to the second bound; wherein (b) and (c) are performed in response to said second cone being a leaf of the cone tree structure and said second bound being a leaf of the bound tree structure. 32. The computer-readable medium of computing the normalized cone size for the first cone based on cone parameters associated with the first cone stored in the cone tree structure. 33. The computer-readable medium of reading the normalized cone size corresponding to the first cone from a memory. 34. A computer-readable medium comprising a plurality of computer program instructions, wherein the plurality of computer program instructions are executable by one or more processors to perform:
searching a cone hierarchy and a bounding hierarchy to determine one or more visible objects for one or more cones of the cone hierarchy, wherein, said searching the cone hierarchy and the bounding hierarchy comprises searching a first cone of the cone hierarchy with respect to a first bound of the bound hierarchy, wherein said searching the first cone with respect to the first bound comprises:
estimating a bound size for the first bound;
comparing the bound size to a cone size corresponding to the first cone;
searching subbounds of the first bound with respect to the first cone if the bound size is larger than the cone size;
transmitting an indication of the one or more visible objects for each of the one or more cones to a rendering agent for rendering and display. 35. The computer-readable medium of 36. The computer-readable medium of 37. The computer-readable medium of searching subcones of the first cone with respect to the first bound if the bound size is smaller than the cone size. 38. The computer-readable medium of (a) determining if said second cone is a leaf of the cone hierarchy and said second bound is a leaf bound of the bounding hierarchy; (b) setting a measurement value associated with the second cone equal to an extent of separation between the second bound and the second cone; (c) setting a visible object attribute associated with the second cone equal to an object pointer value corresponding to the second bound; wherein (b) and (c) are performed in response to said second cone being a leaf of the cone hierarchy and said second bound being a leaf of the bounding hierarchy. 39. The computer-readable medium of computing the cone size for the first cone based on cone parameters associated with the first cone stored in the cone hierarchy. 40. The computer-readable medium of reading the cone size corresponding to the first cone from a memory. 41. The computer-readable medium of receiving a cone pointer which points to the cone hierarchy stored in a memory; and receiving a bound pointer which points to a bounding hierarchy stored in the memory, wherein leaf bounds of the bounding hierarchy approximate a collection of graphical objects. 42. A method for displaying visible objects on a display device, the method comprising:
comparing a bound size for a first bound in a bound tree structure to a cone size corresponding to a first cone in a cone tree structure; searching subbounds of the first bound with respect to the first cone if the bound size is larger than the cone size, wherein said searching subbounds of the first bound with respect to the first cone is used to identify one or more graphical objects which are visible with respect to the first cone; and transmitting an indication of the one or more graphical objects for rendering. 43. The method of searching subcones of the first cone with respect to the first bound if the cone size is larger than the bound size, wherein said searching subcones of the first cone with respect to the first bound is also used to identify the one or more graphics objects which are visible with respect to the first cone. 44. The method of 45. The method of 46. A computer system for displaying visible objects on a display device, the computer system comprising:
a memory configured to store a cone tree structure and a bound tree structure, wherein leaf bounds of the bound tree structure approximate a collection of graphical objects; a processor configured to execute a visibility search algorithm stored in the memory; wherein, in response to an execution of the visibility search algorithm, the processor is configured to compare a bound size of a first bound of a bound tree structure to a cone size of a first cone of a cone tree structure, and to search subbounds of the first bound with respect to the first cone if the bound size is larger than the cone size, wherein said searching subbounds of the first bound with respect to the first cone is used to identify one or more objects which are visible with respect to the first cone, wherein the processor is further configured to transmit an indication of the one or more graphical objects for rendering. 47. The computer system of 48. The computer system of 49. The method of 50. A method for displaying visible objects on a display device, the method comprising:
(a) receiving a hierarchy of boundary information for a plurality of three-dimensional graphical objects; (b) receiving a hierarchy of cones; and (c) determining whether a particular cone and a particular bound intersect, and in response to determining that the particular cone and the particular bound intersect:
determining a relative size value for the particular cone and the particular bound; and
repeating (c) for a subcone of the particular cone if the relative size of the particular cone is larger than the relative size of the particular bound; or
repeating (c) for a subbound of the particular bound if the relative size of the particular bound is larger than the relative size of the particular cone.
Descripción [0052] While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that the drawings and detailed description thereto are not intended to limit the invention to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. Please note that the section headings used herein are for organizational purposes only and are not meant to limit the description or claims. The word “may” is used in this application in a permissive sense (i.e., having the potential to, being able to), not a mandatory sense (i.e., must). Similarly, the word include, and derivations thereof, are used herein to mean “including, but not limited to.” [0053]FIG. 2A presents one embodiment of a graphical computing system [0054]FIG. 2B is a block diagram illustrating one embodiment of graphical computing system [0055] In some embodiments, a 3-D graphics accelerator [0056] 3-D graphics accelerator [0057] The computer system [0058] Visualization Software Architecture [0059]FIG. 3 illustrates one embodiment of the visualization software. In an initial step [0060] In one embodiment of the visualization software, the viewpoint in the graphical environment may be changed in response to user input. For example, by manipulating mouse [0061] In step [0062] In some embodiments, objects may be modeled as opaque convex polytopes. A three-dimensional solid is said to be convex if any two points in the solid (or on the surface of the solid) may be connected with a line segment which resides entirely within the solid. Thus a solid cube is convex, while a donut (i.e. solid torus) is not. A polytope is an object with planar sides (e.g. cube, tetrahedron, etc.). The methodologies described herein for opaque objects naturally extend to transparent or semi-transparent objects by not allowing such objects to terminate a cone computation. Although not all objects are convex, every object can be approximated as a union of convex polytopes. It is helpful to note that the visible-object-set computation does not require an exact computation, but rather a conservative one. In other words, it is permissible to over-estimate the set of visible objects. [0063] Constructing the Object Hierarchy [0064] Initially, the objects in a scene may be organized into a hierarchy that groups objects spatially. An octree is one possibility for generating the object hierarchy. However, in the preferred embodiment, a clustering algorithm is used which groups nearby objects then recursively clusters pairs of groups into larger containing spaces. The clustering algorithm employs a simple distance measure and thresholding operation to achieve the object clustering. FIGS. [0065] Each object may be bounded, i.e. enclosed, by a corresponding bounding surface referred to herein as a bound. In the preferred embodiment, the bound for each object is a polytope hull (i.e. a hull having planar faces) as shown in FIG. 4B. The hulls H [0066] Since a hull has a surface which is comprised of a finite number of planar components, the description of a hull is intimately connected to the description of a plane in three-space. In FIG. 5A, a two dimensional example is given from which the equation of an arbitrary plane may be generalized. A unit vector n [any vector suffices but a vector of length one is convenient for discussion] defines a line L through the origin of the two dimensional space. By taking the dot product v·n of a vector v with the unit vector n, one obtains the length of the projection of vector v in the direction defined by unit vector n. Thus, given a real constant c, it follows that the equation x·n=c, where x is a vector variable, defines a line M perpendicular to line L and situated at a distance c from the origin along line L. In the context of three-dimensional space, this same equation defines a plane perpendicular to the line L, again displaced distance c from the origin along line L. Observe that the constant c may be negative, in which case the line (or plane) M is displaced from the origin at distance |c| along line L in the direction opposite to unit vector n. [0067] The line x·n=c divides the plane into two half-planes. By replacing the equality in the above equation with an inequality, one obtains the description of one of these half-planes. The equality x·n<c defines the half-plane which contains the negative infinity end of line L. [The unit vector n defines the positive direction of line L.] In three dimensions, the plane x·n=c divides the three-dimensional space into two half-spaces. The inequality x·n<c defines the half-space which contains the negative infinity end of line L. [0068]FIG. 5B shows how a rectangular region may be defined as the intersection of four half-planes. Given four normal vectors n [0069] In three-dimensional space, a rectangular box may be analogously defined as the intersection of six half-spaces. Given six normal vectors n [0070] To construct an object hierarchy, object hulls H [0071] The containing-hulls H [0072] In general, a succession of pairing operations is performed. At each stage, a higher-order set of containing-hulls and corresponding nodes for the object hierarchy are generated. Each node contains the describing vector c for the corresponding containing-hull. At the end of the process, the object hierarchy comprises a binary tree with a single root node. The root node corresponds to a total containing-hull which contains all sub-hulls of all orders including all the original object-hulls. The object hierarchy, because it comprises a hierarchy of bounding hulls, will also be referred to as the hull hierarchy. In the preferred embodiment, the pairing operations are based on proximity, i.e. objects (and hulls of the same order) are paired based on proximity. Proximity based pairing results in a more efficient visible object determination algorithm. This tree of containing hulls provides a computationally efficient, hierarchical representation of the entire scene. For instance, when a cone completely misses a node's containing-hull, none of the node's descendents need to be examined. [0073] Bounding hulls (i.e. containing hulls) serve the purpose of simplifying and approximating objects. Any hierarchy of containing hulls works in principle. However, hierarchies of hulls based on a common set of normal vectors are particularly efficient computationally. A collection of hulls based on a common set of normal vectors will be referred to herein as a fixed-direction or commonly-generated collection. As described above, a polytope hull is described by a bounding system of linear inequalities {x: Nx≦c}, where the rows of the matrix N are a set of normal vectors, and the elements of the vector c define the distances to move along each of the normal vectors to obtain a corresponding side of the polytope. In a fixed-direction collection of hulls, the normal matrix N is common to all the hulls in the collection, while the vector c is unique for each hull in the collection. The problem of calculating the coefficient vector c for a containing hull given a collection of subhulls is greatly simplified when a common set of normal vectors is used. In addition, the nodes of the hull hierarchy may advantageously consume less memory space since the normal matrix N need not be stored in the nodes. In some embodiments, the hull hierarchy comprises a fixed-direction collection of hulls. [0074] In a first embodiment, six normal vectors oriented in the three positive and three negative axial directions are used to generate a fixed-direction hierarchy of hulls shaped like rectangular boxes with sides parallel to the coordinate planes. These axis-aligned bounding hulls provide a simple representation that has excellent local computational properties. It is easy to transform or compare two axis-aligned hulls. However, the approximation provided by axis-aligned hulls tends to be rather coarse, often proving costly at more global levels. [0075] In a second embodiment, eight normal vectors directed towards the corners of a cube are used to generate a hierarchy of eight-sided hulls. For example, the eight vectors (±1,±1,±1) may be used to generate the eight-sided hulls. The octahedron is a special case of this hull family. [0076] In a third embodiment, fourteen normal vectors, i.e. the six normals which generate the rectangular boxes plus the eight normals which generate the eight-sided boxes, are used to generate a hull hierarchy with fourteen-sided hulls. These fourteen-sided hulls may be described as rectangular boxes with comers shaved off. It is noted that as the number of normal vectors and therefore side increases, the accuracy of the hull's approximation to the underlying object increases. [0077] In a fourth embodiment, twelve more normals are added to the fourteen normals just described to obtain a set of twenty-six normal vectors. The twelve additional normals serve to shave off the twelve edges of the rectangular box in addition to the corners which have already been shaved off. This results in twenty-six sided hulls. For example, the twelve normal vectors (±1,±1, 0), (±1,0,±1), and (0,±1,±1) may be used as the additional vectors. [0078] In the examples given above, hulls are recursively grouped in pairs to generate a binary tree. However, in other embodiments, hulls are grouped together in groups of size G, where G is larger than two. In one embodiment, the group size varies from group to group. [0079] Although the above discussion has focussed on the use of polytope hulls as bounds for object and clusters, it is noted that any type of bounding surfaces may be used, thereby generating a hierarchy of bounds referred to herein as a bounding hierarchy or bound tree structure. Each node of the bounding hierarchy corresponds to an object or cluster and stores parameters which characterize the corresponding bound for that object or cluster. For example, polynomial surfaces such as quadratic surfaces may be used to generate bounds for objects and/or clusters. Spheres and ellipsoids are examples of quadratic surfaces. [0080] Cones in Visible Object Determination [0081] In addition to the bounding hierarchy (e.g. hull hierarchy) discussed above, the visualization software makes use of a hierarchy of spatial cones. An initial cone which may represent the view frustum is recursively subdivided into a hierarchy of sub-cones. Then a simultaneous double recursion is performed through the pair of trees (the object tree and cone tree) to rapidly determine the set of visible objects. This cone-based method provides a substantial computational gain over the prior art method based on ray-casting. [0082] Cones discretize the spatial continuum differently than rays. Consider the simultaneous propagation of all possible rays from a point and the ensuing spherical wavefront. The first object encountered by each ray is visible. If consideration is restricted to those rays that form a cone, the same observation still applies. The first object encountered by the cone's wavefront is visible. Now, if the view frustum is partitioned into some number of cones, the objects visible from the viewpoint can be determined up to the resolution of the cones. [0083]FIG. 6 illustrates a two-dimensional cone C in a two-dimensional environment. Cone C is defined by the region interior to the rays R [0084] It is noted that the cone-based object visibility query (modeled on the wavefront propagation concept) is an inherently spatial computation. Thus, the object visibility query for subcone C [0085] Polyhedral Cones [0086] The spatial cones used in the preferred embodiment are polyhedral cones. The generic polyhedral cone has a polygonal cross-section. FIG. 7 gives two examples of polyhedral cones. The first polyhedral cone PC [0087] A polyhedral cone is constructed by intersection of multiple half-spaces. For example, solid cone PC [0088] Thus, a polyhedral cone emanating from the origin is defined as the set of points satisfying a system of linear inequalities Sx≦0. [There is no loss of generality in assuming the origin to be the viewpoint.] According to this definition, half-spaces, planes, rays, and the origin itself may be considered as polyhedral cones. In addition, the entire space may be considered to be a polyhedral cone, i.e. that cone which is defined by an empty matrix S. [0089] Distance Measurement [0090] In view of the discussion concerning wave propagation, the distance of an object, hull, or bound from a particular viewpoint is defined to be the minimum distance to the object, hull, or bound from the viewpoint. So, assuming a viewpoint at the origin, the distance of the object, hull, or bound X from the viewpoint is defined as [0091] where ∥x∥ is the norm of vector x. When the object, hull, or bound X is empty, the distance may be taken to be positive infinity. [0092] Any vector norm may be chosen for the measurement of distance. In one embodiment, the Euclidean norm is chosen for distance measurements. The Euclidean norm results in a spherically shaped wavefront. Any wavefront shape may be used as long as it satisfies a mild “star-shape” criterion, i.e. the entire boundary of the wavefront is unobstructed when viewed from the origin. All convex wavefronts satisfy this condition, and many non-convex ones do as well. In general, the level curves of a norm are recommended as the wavefront shapes. From a computational standpoint, the spherical wavefront shape given by the L [0093] Cones and Visibility [0094] From a viewpoint located within a large set of objects, there exists at least one point (on some object) nearest to the viewpoint. Since that point (or set of points) is closest to the viewpoint, nothing can occlude the view of that point (or those points). This implies that the object (or objects) containing the nearest point (or points) is (are) at least partially visible. [0095] Now, consider an arbitrary cone K emanating from the origin as a viewpoint. The unobstructed visibility argument holds even if all distance measurements are restricted to points that fall within the cone. Define the distance of an object, hull, or bound X relative to the cone K as [0096] If the distance f [0097] It is noted that rays may be viewed as degenerate cones that emanate from the viewpoint and pass through the center of each pixel. The nearest object along each ray is visible and thus determines the value of the corresponding pixel. Similarly, it is possible to construct cones which emanate from the viewpoint and cover each pixel. For example, two or more cones with triangular cross-section may neatly cover the area of a pixel. The nearest object within each cone is visible and contributes to the value of the corresponding pixel. [0098] As discussed above, the ray-based methods of the prior art are able to detect objects only up the resolution of the ray sample. Small visible objects or small portions of larger objects may be missed entirely due to insufficient ray density. In contrast, cones can completely fill space. Thus, the cone-based method disclosed herein may advantageously detect small visible objects or portions of objects that would be missed by a ray-based method with equal angular resolution. [0099] Generalized Separation Measurement [0100] For the purposes of performing a visibility search procedure, it is necessary to have a method for measuring the extent of separation (or conversely proximity) of objects, bounds, or hulls with respect to cones. There exists a great variety of such methods in addition to those based on minimizing vector norms defined above. As alluded to above, a measurement value indicating the extent of separation between a set X and a cone K may be obtained by propagating a wavefront internal to the cone from the vertex of the cone and observing the radius of first interaction of the internal wavefront with the set X. As mentioned above, the wavefront may satisfy a mild “star shape” condition: the entire boundary of the wavefront is visible from the vertex of the cone. [0101] In one embodiment, the measurement value is obtained by computing a penalty of separation between the set X and the cone K. The penalty of separation is evaluated by minimizing an increasing function of separation distance between the vertex of the cone K and points in the intersection of the cone K and set X. For example, any positive power of a vector norm gives such an increasing function. [0102] In another embodiment, the measurement value is obtained by computing a merit of proximity between the set X and the cone K. The merit of proximity is evaluated by maximizing a decreasing function of separation distance between the vertex of the cone K and points in the intersection of the cone K and set X. For example, any negative power of a vector norm gives such a decreasing function. [0103] A Cone Hierarchy [0104] In some embodiment, the visibility determination method uses a hierarchy of cones in addition to the hierarchy of hulls described above. The class of polyhedral cones is especially well suited for generating a cone hierarchy: polyhedral cones naturally decompose into polyhedral subcones by the insertion of one or more separating planes. The ability to nest cones into a hierarchical structure allows a rapid examination of object visibility. As an example, consider two-neighboring cones that share a common face. By taking the union of these two cones, a new composite cone is generated. The composite cone neatly contains its children, and is thus capable of being used in querying exactly the same space as its two children. In other words, the children cones share no interior points with each other and they completely fill the parent without leaving any empty space. [0105] A typical display and its associated view frustum has a rectangular cross-section. There are vast array of possibilities for tessellating this rectangular cross-section to generate a system of sub-cones. For example, the rectangle naturally decomposes into four rectangular cross-sections, or two triangular cross-sections. Although these examples illustrate decompositions using regular components, irregular components may be used as well. [0106] FIGS. [0107] The triangular hierarchical decomposition shown in FIGS. [0108] It is noted that any cone decomposition strategy may be employed to generate a cone hierarchy. In a second embodiment, the view frustum is decomposed into four similar rectangular cones; each of these subcones is decomposed into four more rectangular subcones, and so on. This results in a cone tree with four-fold branches. [0109] Discovering the Set of Visible Objects [0110] Once the hull hierarchy and the cone hierarchy have been constructed, the set of visible objects may be computed with respect to the current viewpoint. In one embodiment, the visible object set is repeatedly recomputed for a succession of viewpoints, viewing directions, video frames, etc. The successive viewpoints and/or viewing directions may be specified by a user through an input device such as a mouse, joystick, keyboard, trackball, head-position sensor, eye-orientation sensor, or any combination thereof. The visible object determination method may be organized as a simultaneous search of the hull tree and the cone tree. The search process may involve recursively performing cone-hull queries. Given a cone node K and a hull node H, a cone-hull query on cone K and hull H investigates the visibility of hull H and its descendent hulls with respect to cone K and its descendent cones. The search process has a computational complexity of order log M, where M equals the number of cone nodes times the number of hull nodes. In addition, many cone-hull queries can occur in parallel allowing aggressive use of multiple processors in constructing the visible-object-set. [0111] Viewing the Scene [0112] Independently, and also concurrently, the set of visible objects from the current viewpoint may be rendered on one or more displays. The rendering can occur concurrently because the visible-object-set remains fairly constant between frames in a walkthrough environment. Thus the previous set of visible objects provides an excellent approximation to the current set of visible objects. [0113] Managing the Visible-Object-Set [0114] The visualization software may manage the visible-object-set. Over time, as an end-user navigates through a model, just inserting objects into the visible object set would result in a visible object set that contains too many objects. To ensure good rendering performance, the visualization process may remove objects from the visible object set when those objects no longer belong to the set—or soon thereafter. A variety of solutions to object removal are possible. One solution is based on object aging. The system removes any object from the visible object set that has not been rediscovered by the cone query within a specified number of redraw cycles. [0115] Computing Visibility Using Cones [0116] Substantial computational leverage is provided by recursively searching the hierarchical tree of cones in conjunction with the hierarchical tree of hulls. Whole groups of cones may be tested against whole groups of hulls in a single query. For example, if a parent cone does not intersect a parent hull, it is obvious that no child of the parent cone can intersect any child of the parent hull. In such a situation, the parent hull and all of its descendants may be removed from further visibility considerations with respect to the parent cone. [0117] Visibility Search Algorithm [0118] In the preferred embodiment, the visibility search algorithm may be realized by a visibility search program. The visibility search program may be stored in memory [0119] The recursive search of the two trees provides a number of opportunities for aggressive pruning of the search space. Central to the search is the object-cone distance measure defined above, i.e. given a cone K and an object (or hull) X, the object-cone distance is defined as [0120] It is noted that this minimization is in general a nonlinear programming problem since the cones and object hulls are defined by constraint equations, i.e. planes in three-space. If the vector norm ∥x∥ is the L [0121] The recursive search starts with the root H of the hull tree and the root cone C of the cone tree (see FIGS. 4 and 9). Remember that each node of the hull tree specifies a bounding hull which contains the hulls of all its descendant nodes. Initially the distance between the root cone and the root hull is computed. If that distance is infinite, then no cone in the cone hierarchy intersects any hull in the hull hierarchy and there are no visible objects. If the distance is finite, then further searching may be performed. Either tree may be refined at this point. [0122] The pruning mechanism is built upon several basic elements. A distance measurement function computes the distance f [0123] To facilitate the search process, each leaf-cone, i.e. each terminal node of the cone tree, is assigned an extent value which represents its distance to the closest known object-hull. [An object-hull is a hull that directly bounds an object. Object-hulls are terminal nodes of the hull tree.] Thus, this extent value may be referred to as the visibility distance. The visibility distance of a leaf-cone is non-increasing, i.e. it decreases as closer objects (i.e. object hulls) are discovered in the search process. Visibility distances for all leaf-cones are initialized to positive infinity. In addition to a visibility distance value, each leaf-cone node is assigned storage for a currently visible object. This object attribute may be initialized with a reserved value denoted NO_OBJECT which implies that no object is yet associated with the leaf-cone. In another embodiment, the object attribute may be initialized with a reserved value denoted BACKGROUND which implies that a default scene background is associated with the leaf-cone. [0124] In addition, each non-leaf cone, i.e. each cone at a non-final refinement level, is assigned an extent value which equals the maximum of the extent values of its sub-cones. Or equivalently, the extent value for a non-leaf cone equals the maximum of the visibility distance values of its leaf-cone descendents. These extent values are also referred to as visibility distance values. The visibility distance values for all non-leaf cones are initialized to positive infinity also (consistent with initialization of the leaf-cones). Suppose a given non-leaf cone K and a hull X achieve a cone-object distance f [0125] The following code fragment illustrates the beginning of the search process according to one embodiment of the visibility search algorithm. The variables hullTree and coneTree point to the root nodes of the hull tree and cone tree respectively. [0126] The function Dist evaluates the distance between the root hull and the root cone. If this distance is less than positive infinity, the function findVisible is called with the root hull, root cone, and their hull-cone distance as arguments. The function findVisible performs the recursive search of the two trees. [0127] FIGS. findVisible(H, C, d [0128] where H is a hull node to be explored against the cone node C. The value d [0129] In step [0130] If the hull H and cone C are not both leaves, step [0131] In step [0132] In step [0133] If, in step [0134] In step [0135] In step [0136] Since the visibility distance values for subcones C [0137] If, in step [0138] In step [0139] In step [0140] In step [0141] In step [0142] If Size_H is greater than (or greater than or equal to) Size_C, the subhulls of hull H may be explored with respect to cone C in step [0143] In step [0144] In step [0145] As explained above, the visibility distance value assigned to each cone in the cone tree is set equal the maximum of the visibility distance values assigned to its subcone children. Thus, if a given hull achieves a distance to a cone which is larger than the cone's current visibility distance value, all of the cone's leaf-cone descendents have already “discovered” objects closer than the given hull and any of its leaf-hull descendents. The given hull node may not be searched with respect to this cone. [0146] A cone's visibility distance value decreases as the recursion tests more and more object-cone leaf pairs. As nearby objects are discovered, a cone's visibility distance value decreases and the probability of skipping unpromising hull nodes increases. A leaf in the hull tree bounds the volume of the associated object and also approximates that object's contents. Thus, cone visibility distance values, set during recursion, are usually not the real distances to objects but a conservative approximation of those distances. If the conservative approximation is inadequate for use in an application, then that application can invoke a higher precision computation of object-cone distance to determine the visibility distance values. [0147] Throughout the above discussion of the visibility search algorithm it has been assumed that the function Dist used to compute cone-hull separation distance is based on minimizing an increasing function of separation distance between the vertex of the given cone and points in the intersection of the given cone and the given bound/hull. However, it is noted that the function Dist may be programmed to compute a merit of proximity between a given cone and given bound/hull. The resulting merit value increases with increasing proximity and decreases with increasing separation, converse to the typical behavior of a distance function. In this case, the visibility search algorithm performs a search of bound/hull H against cone K only if the merit value of separation between cone K and bound/hull H is greater than the current merit value associated with cone K. Furthermore, after a search of subcones of cone K is completed, the merit value associated with the cone K is updated to equal the minimum of the merit values of its subcone children. [0148] In general, the function Dist determines a cone-hull measurement value of separation by computing the extremum (i.e. minimum or maximum) of some monotonic (increasing or decreasing) function of separation between the vertex of the cone K and points in the intersection of cone K and bound/hull H. The search of cone K against a bound/hull H is conditioned on the bound/hull H achieving a cone-hull measurement value with respect to cone K which satisfies an inequality condition with respect to measurement value assigned to cone K. The sense of the inequality, i.e. less than or greater than, depends on the whether the function Dist uses an increasing or decreasing function of separation. [0149] While the search of the hull and cone hierarchies described above assumes a recursive form, it is noted that any search strategy may be employed. In one alternate embodiment, the hull and/or cone hierarchies are searched iteratively. Such a brute force solution may be advantageous when a large array of processors is available to implement the iterative search. In another embodiment, a level-order search is performed on the hull and/or cone hierarchies. [0150] Size Conditioned Tree Search [0151] As described above in connection with step [0152] If the hull size Size_H is larger than the cone size Size_C as suggested by FIG. 10D, on average, the probability of at least one subhull of hull H having an empty intersection with cone C is larger than the probability of at least one subcone of cone C having an empty intersection with hull H. Thus, in this case, it may be more advantageous to explore the subhulls H [0153] If the hull size Size_H is smaller than the cone size Size_C as suggested by FIG. 10E, on average, the probability of at least one subhull of hull H having an empty intersection with cone C is smaller than the probability of at least one subcone of cone C having an empty intersection with hull H. Thus, in this case, it may be more advantageous to explore the subcones C [0154] By selecting the larger entity (hull or cone) for refinement, the findVisible function may more effectively prune the combined hull-cone tree, and determine the set of visible objects with increased efficiency. [0155] Searching Subhulls in Order of Proximity to a Hull [0156] In step [0157] If subhull H [0158] In steps [0159] The fixed-order subhull search shown in steps [0160] Method for Displaying Visible Objects [0161] One embodiment of a method for displaying visible objects in a graphics environment is described in the flowchart of FIG. 11. A visibility search algorithm executing on one or more processors (e.g. CPU [0162] In step [0163] In step [0164] In step [0165] In one embodiment, graphics accelerator [0166] In a second embodiment, CPU [0167] In a third embodiment, CPU [0168] Step [0169] In step [0170] In one embodiment, the normalized cone size may comprise an estimated area of projection of the first cone on the surface of a sphere of fixed radius (e.g. radius one) centered at the vertex of the first cone. In a second embodiment, the normalized cone size may be a cross sectional area of the first cone. For example, the visibility search algorithm may compute the area of the cone's cross section generated by a plane normal to the cone's axis at some fixed distance (e.g. distance one) from the cone's vertex. In a third embodiment, the normalized cone size of the first cone may be some function of its refinement level number. For example, cones at the first, second, and third refinement levels may of the cone tree may have sizes proportional to ½, ¼ and ⅛ respectively. At the R [0171] In step [0172] In step [0173] In step [0174] In step [0175] In one embodiment of step [0176] (a) computing a first cone-bound separation value (e.g. a distance value as described above in conjunction with FIGS. [0177] (b) determining whether the first cone-bound separation value satisfies an inequality condition with respect to a measurement value (e.g. the visibility distance value described above) associated with the first cone; and [0178] (c) searching the first subbound with respect to said first cone if the inequality condition is satisfied. [0179] In one embodiment of step [0180] (c) computing a first cone-bound separation value (e.g. a distance value as described above in connection with FIG. 10A-C) for the first subcone with respect to the first bound; [0181] (d) determining whether the first cone-bound separation value satisfies an inequality condition with respect to a measurement value associated with the first subcone; and [0182] (e) searching the first subcone with respect to the first bound if the inequality condition is satisfied. [0183] The visibility search algorithm may update the measurement value associated with the first cone with the extremum (e.g. maximum or minimum) of the measurement values associated with the subcones after searching the subcones with respect to the first bound. The choice of the maximum as the extremum is associated with embodiments which compute cone-bound separation based on an increasing function of separation. The choice of minimum as the extremum is associated with embodiments which compute cone-bound separation based on a decreasing function of separation. [0184] As described above in connection with FIGS. [0185] In some embodiments, the cone-bound separation value for a given cone and bound may be determined by minimizing an increasing function of separation distance between the vertex of the cone and points in the intersection of the cone and the bound. In this case, the inequality condition referred to above is said to be satisfied when the cone-bound separation value is less than the measurement value associated with the cone. [0186] The increasing function of separation distance may be specified by a vector norm. For example, the expression ∥s∥ defines an increasing function of separation distance, where s is a displacement vector representing the vector difference between the vertex of the first cone and an arbitrary point in the intersection of the first cone and the first bound, and ∥·∥ denotes a vector norm. Examples, of vector norms include the L [0187] In one embodiment, the cone-bound separation value for a given cone and bound may be computed by solving a linear programming problem using the linear constraints given by normal matrix S for the cone, and the linear constraints given by the normal matrix N and the extent vector c for the bound. Recall the discussion in connection with FIGS. [0188] In an alternate embodiment, the cone-bound separation value comprises a merit of proximity (i.e. closeness) between the cone and the bound which is determined by maximizing a decreasing function of separation distance between the vertex of the cone and points in the intersection of the cone and the bound. In this case, the inequality condition referred to above is said to be satisfied when the cone-bound separation value is greater than the measurement value associated with the cone. [0189] A Terminal Cone-Bound Pair [0190] As the dual tree search step [0191] A Terminal Cone With a Non-Terminal Bound [0192] As the dual tree search step [0193] A Terminal Hull With a Non-Terminal Cone [0194] As the dual tree search step [0195] In some embodiments, the leaf-cones subtend angular sectors larger than one pixel. Thus, after termination of the visibility search algorithm described above, the leaf-cones may be further processed by a ray-based exploration method to determine the values for individual pixels within leaf-cones. [0196] Although the search of the bound tree structure and the cone tree structure described above assumes a recursive form, alternate embodiments are contemplated where a level-order search or iterative search is performed on one or both of the bounding tree structure and cone tree structure. [0197] Additional Embodiments [0198] A wide variety of system and method embodiments are contemplated in addition to those discussed above. In one embodiment, a method for displaying visible objects on a display device may comprise the following operations: [0199] (1) comparing a bound size for a first bound in a bound tree structure to a cone size corresponding to a first cone in a cone tree structure; [0200] (2) searching subbounds of the first bound with respect to the first cone if the bound size is larger than the cone size, wherein the operation of searching subbounds of the first bound with respect to the first cone is used to identify one or more graphical objects which are visible with respect to the first cone; and [0201] (3) transmitting an indication of the one or more graphical objects for rendering. [0202] The method may further comprise searching subcones of the first cone with respect to the first bound if the cone size is larger than the bound size, wherein the operation of searching subcones of the first cone with respect to the first bound is also used to identify the one or more graphics objects which are visible with respect to the first cone. The method may also include computing the cone size and/or the bound size. Alternatively, an external system/device may compute the cone size and/or bound size, and provide the cone size and/or bound size as inputs for the comparison operation (1). The method may be implemented on one or more processors which execute program code stored in a memory subsystem. [0203] Method For Constructing a Bounding Hierarchy [0204]FIG. 15 illustrates the construction of a bounding hierarchy (i.e. a bounding tree structure) from a collection of objects. The collection of objects may be accessed from memory [0205] In step [0206] Although the construction of the cone hierarchy above has been described in terms of recursive clustering, it is noted alternative embodiments are contemplated which use other forms of clustering such as iterative clustering. [0207] Computing the Cone Restricted Distance Function [0208] Recall that evaluation of the cone-hull distance f [0209] It is also noted that cone-hull separation may be measured by maximizing an decreasing function separation such as ∥x∥ [0210] The use of a hierarchy of cones instead of a collection of rays is motivated by the desire for computational efficiency. Thanks to early candidate pruning that results from the double recursion illustrated earlier, fewer geometric queries are performed. These queries however are more expensive than the queries used in the ray casting method. Therefore, the cone query calculation may be designed meticulously. A sloppy algorithm could end up wasting most of the computational advantage provided by improvements in the dual tree search. For the linear programming case, a method for achieving a computationally tight query will now be outlined. [0211] A piecewise-linear formulation of distance f min(v subject to Ax≦b, Sx≦0. [0212] The vector v is some member of the cone that is polar to the cone C. For instance, V=−S max(b subject to [0213] The dual objective value, b [0214] In the preferred embodiment, the bounding hulls have sides normal to a fixed set of normal vectors. Thus, the matrix A {(y,z): [0215] is associated with the cone. (In one embodiment, this polyhedron has seventeen dimensions. Fourteen of those dimensions come from the type of the fixed-direction bounding hull and an three additional dimensions come from the cone.) Since the polyhedron depends only on the cone matrix S, it is feasible to completely precompute the extremal structure of the polygon for each cone in the cone hierarchy. By complementary slackness, the vertices of the polyhedron will have at most three elements. The edges and extremal rays will have at most four non-zero elements. An abbreviated, simplex-based, hill-climbing technique can be used to quickly solve the query in this setting. [0216] In one embodiment, the entire space is tessellated with cones, and visible objects are detected within the entire space. After this entire-space visibility computation, the set of visible objects may be culled to conform to the current view frustum, and the visible objects which survive the frustum culling may be rendered and displayed. [0217] In an alternative embodiment, a less aggressive approach may be pursued. In particular, by determining beforehand a collection of the cones in the cone hierarchy which correspond to the view frustum in its current orientation, only this collection may be included in the visible-object-set computation. [0218] Memory Media [0219] As described above, the visibility software and visibility search program may be stored in memory [0220] In one embodiment, the visibility search program is implemented as part of an operating system. In a second embodiment, the visibility search program is implemented as a dynamic link library (DLL). In a third embodiment, the visibility search program is implemented as part of a device driver (e.g. a device driver for graphics accelerator [0221] In a fourth embodiment, the visibility search program is implemented as part of a JAVA 3D™ virtual machine or JAVA 3D API (application programming interface) which executes on processor [0222] Multiple Objects Per Cone [0223] In one embodiment of the visibility search algorithm, one or more nearest objects may be identified for each leaf cone (i.e. terminal cone). If each of the leaf cones have the resolution of a pixel, then the strategy of identifying the single nearest object in each leaf cone may be sufficient to guarantee detection of all visible objects. However, the visibility search of a cone hierarchy which is refined to pixel resolution is computationally expensive. The computational expense may be decreased by having fewer levels of refinement in the cone hierarchy. But fewer levels of refinement implies that the size of the leaf cones is larger. As the size of the leaf cones increases, there is an increasing probability that two or more objects will be visible to a single leaf cone, i.e. that the nearest object is not the only object visible to the cone. Therefore, the single-nearest-object strategy has an increased probability of reporting less than the full set of visible objects as the size of the leaf-cones increases, or equivalently, when fewer levels of cone refinement are used in the cone hierarchy. [0224] In order to increase the probability of capturing the full set of visible objects, the visibility search algorithm may identify the first K nearest objects for each leaf cone, where K is an integer greater than or equal to two. Advantageously, the integer K may be a function of cone size. Thus, if the leaf cones have a resolution close to pixel resolution, integer K may be close to one. Conversely, if the leaf cone resolution is larger than pixel resolution, integer K may be larger. [0225] A leaf cone node in the cone tree may store up to K object pointers, and K distance values D D [0226] These K distance values may be initialized to positive infinity. The largest distance value D [0227] In some embodiments, computational efficiency may be maximized along the axis of high-cone-resolution/low-K-value on the one hand and low-cone-resolution/high-K-value on the other. [0228] Adaptive Refinement of the Cone Hierarchy [0229] In the foregoing discussion, the cone hierarchy is described as being constructed prior to initiation of the search for visible objects by the visibility search algorithm, and remains static during the search. Another alternative is to adaptively refine the cone hierarchy during the search procedure. In this fashion, the cone hierarchy may not waste storage for cones which will never interact with any objects. The cone hierarchy may be refined in response to user inputs. For example, cones which correspond to the user's current direction of gaze may warrant additional refinement. A given cone may remain unrefined until the search procedure discovers a bound which interacts with the given cone, at which time the cone may be refined. The refinement of a given cone may be further refined as additional interacting objects/bounds are discovered in order to more adequately distinguish the objects. In the context where objects are in motion, the movement of an object into a given cone's field of view may induce increased refinement of the given cone. If the user in a virtual environment stops to look at a given object, the cones defining that object may be increasingly refined. [0230] Refinement of the cone hierarchy may be subject to the availability of computational cycles. According to the paradigm of successive warming, the initial cone tree may have only one or a few cones allowing a crude initial estimate of visible object set to be immediately displayed. As computational cycles become available the cone hierarchy may be successively refined and searched in order to provide an increasingly accurate display of the visible object set. [0231] In general the cones of the cone hierarchy may be at differing levels of refinement. Cone refinement may be permitted only if the cone interacts with an object or bound (e.g. hull). Adaptive refinement of a cone may be terminated when the cone resolution equals that of a pixel or when no object occurs in the cone. [0232] In one embodiment, a combination of fixed refinement and adaptive refinement of the cone hierarchy may be used. [0233] In some embodiments, the visibility search algorithm may combine adaptive refinement of the cone hierarchy and identification of the K nearest objects/bound for each cone, where K changes as the refinement level changes. [0234] Non-Occluding Objects [0235] Non-occluding objects are objects which do not totally occlude (i.e. block visibility) of other objects. For example, a transparent, semi-transparent, or translucent object may be a non-occluder. A screen door, tinted glass, a window with slats may be classified as non-occluders. Objects behind a non-occluder may be partially visible. In some embodiments, certain modifications of the visibility search algorithm may allow for the presence of non-occluding objects (NOOs) in the collection of objects to be searched. In particular, the visibility search algorithm may be configured to search for the first K nearest occluding objects and any NOO closer than the K [0236] Although the embodiments above have been described in considerable detail, other versions are possible. Numerous variations and modifications will become apparent to those skilled in the art once the above disclosure is fully appreciated. It is intended that the following claims be interpreted to embrace all such variations and modifications. Note the headings used herein are for organizational purposes only and are not meant to limit the description provided herein or the claims attached hereto. [0027] The foregoing, as well as other objects, features, and advantages of this invention may be more completely understood by reference to the following detailed description when read together with the accompanying drawings in which: [0028]FIG. 1 illustrates the ray-based method of visible object detection according to the prior art; [0029]FIG. 2A illustrates one embodiment of a graphical computing system for performing visible object determination; [0030]FIG. 2B is a block diagram illustrating one embodiment of the graphical computing [0031]FIG. 3 illustrates several main phases of one embodiment of a visualization program; [0032]FIG. 4A illustrates a collection of objects in a graphics environment; [0033]FIG. 4B illustrates a first step in one embodiment of a method for forming a hull hierarchy, i.e. the step of bounding objects with containing hulls and allocating hull nodes for the containing hulls; [0034]FIG. 4C illustrates one embodiment of the process of grouping together hulls to form higher order hulls, and allocating nodes in the hull hierarchy which correspond to the higher order hulls; [0035]FIG. 4D illustrates the culmination of one embodiment of the recursive grouping process wherein all objects are contained in a universal containing hull which corresponds to the root node of the hull hierarhcy; [0036]FIG. 5A illustrates the mathematical expressions which describe lines and half-planes in two dimensional space; [0037]FIG. 5B illustrates the description of a rectangular region as the intersection of four half-planes in a two dimensional space; [0038]FIG. 6 illustrates a two-dimensional cone partitioned into a number of subcones which interact with a collection of objects by means of wavefronts propagating within each of the subcones; [0039]FIG. 7 illustrates polyhedral cones with rectangular and triangular cross-section emanating from the origin; [0040]FIG. 8A illustrates mathematical expressions which describe a line through the origin and a corresponding half-plane given a normal vector in two-dimensional space; [0041]FIG. 8B illustrates the specification of a two-dimensional conic region as the intersection of two half-planes; [0042] FIGS. [0043] FIGS. [0044]FIG. 10D illustrates a cone C which has a small normalized size compared to a bound hull H; [0045]FIG. 10E illustrates a hull H which has a small normalized size compared to a cone C; [0046]FIG. 10F illustrates one embodiment of step [0047]FIG. 11 illustrates one embodiment of a visibility search method for identifying and displaying visible objects in a graphics environment; [0048]FIG. 12 illustrates processing steps which may be performed when the visibility search method arrives at a terminal cone and a terminal bound; [0049]FIG. 13 illustrates processing steps which may be performed when the visibility search method arrives at a terminal cone and a non-terminal bound; [0050]FIG. 14 illustrates processing steps which may be performed when the visibility search arrives at a terminal bound and a non-terminal cone; [0051]FIG. 15 illustrates one embodiment of the process of recursively clustering a collection of objects to form a bounding hierarchy; [0002] 1. Field of the Invention [0003] The present invention relates generally to the field of computer graphics, and more particularly, to the problem of determining the set of objects (and portions of objects) visible from a defined viewpoint in a graphics environment. [0004] 2. Description of the Related Art [0005] Visualization software has proven to be very useful in evaluating three-dimensional designs long before the physical realization of those designs. In addition, visualization software has shown its cost effectiveness by allowing engineering companies to find design problems early in the design cycle, thus saving them significant amounts of money. Unfortunately, the need to view more and more complex scenes has outpaced the ability of graphics hardware systems to display them at reasonable frame rates. As scene complexity grows, visualization software designers need to carefully use the rendering resource provided by graphic hardware pipelines. [0006] A hardware pipeline wastes rendering bandwidth when it discards rendered triangle work. Rendering bandwidth waste can be decreased by not asking the pipeline to draw triangles that it will discard. Various software methods for reducing pipeline waste have evolved over time. Each technique reduces waste at a different point within the pipeline. As an example, software culling of objects falling outside the view frustum can significantly reduce discards in a pipeline's clipping computation. Similarly, software culling of backfacing triangles can reduce discards in a pipeline's lighting computation. [0007] The z-buffer is the final part of the graphics pipeline that discards work. In essence, the z-buffer retains visible surfaces, and discards those not visible because they are behind another surface (i.e. occluded). As scene complexity increases, especially in walk-through and CAD environments, the number of occluded surfaces rises rapidly and as a result the number of surfaces that the z-buffer discards rises as well. A frame's average depth complexity determines roughly how much work (and thus rendering bandwidth) the z-buffer discards. In a frame with a per-pixel depth complexity of d the pipeline's effectiveness is 1/d. As depth complexity rises, the hardware pipeline thus becomes proportionally less and less effective. [0008] Software occlusion culling has been proposed as an additional tool for improving rendering effectiveness. A visualization program which performs occlusion culling effectively increases the overall rendering bandwidth of the graphics hardware by not asking the hardware pipeline to draw occluded objects. Computing a scene's visible objects is the complementary problem to that of occlusion culling. Rather than removing occluded objects from the set of objects in a scene or frustum-culled scene, a program instead computes which objects are visible and instructs the rendering hardware to draw just those. A simple visualization program can compute the set of visible objects and draw those objects from the current viewpoint, thus allowing the pipeline to focus on removing backfacing polygons and the z-buffer to remove any non-visible surfaces of those objects. [0009] One technique for computing the visible object set uses ray casting as shown in FIG. 1. RealEyes [Sowizral, H. A., Zikan, K., Esposito, C., Janin, A., Mizell, D., “RealEyes: A System for Visualizing Very Large Physical Structures”, SIGGRAPH '94, Visual Proceedings, 1994, p. 228], a system that implemented the ray casting technique, was demonstrated in SIGGRAPH 1994's BOOM room. At interactive rates, visitors could “walk” around the interior of a Boeing 747 or explore the structures comprising Space Station Freedom's lab module. [0010] The intuition for the use of rays in determining visibility relies on the properties of light. The first object encountered along a ray is visible since it alone can reflect light into the viewer's eye. Also, that object interposes itself between the viewer and all succeeding objects along the ray making them not visible. In the discrete world of computer graphics, it is difficult to propagate a continuum of rays. So a discrete subset of rays is invariably used. Of course, this implies that visible objects or segments of objects smaller than the resolution of the ray sample may be missed and not discovered. This is because rays guarantee correct determination of visible objects only up to the density of the ray-sample. FIG. 1 illustrates the ray-based method of visible object detection. Rays that interact with one or more objects are marked with a dot at the point of their first contact with an object. It is this point of first contact that determines the value of the screen pixel corresponding to the ray. Also observe that the object [0011] Visible-object determination has its roots in visible-surface determination. Foley et al. [Foley, J., van Dam, A., Feiner, S. and Hughes, J. Computer Graphics: Principles and Practice, 2nd ed., Addison-Wesley, Chapter 15, pp.649-718, 1996] classify visible-surface determination approaches into two broad groups: image-precision and object-precision algorithms. Image precision algorithms typically operate at the resolution of the display device and tend to have superior performance computationally. Object precision approaches operate in object space—usually performing object to object comparisons. [0012] A prototypical image-precision visible-surface-determination algorithm casts rays from the viewpoint through the center of each display pixel to determine the nearest visible surface along each ray. The list of applications of visible-surface ray casting (or ray tracing) is long and distinguished. Appel [“Some Techniques for Shading Machine Rendering of Solids”, SJCC'68, pp. 37-45, 1968] uses ray casting for shading. Goldstein and Nagel [Mathematical Applications Group, Inc., “3-D Simulated Graphics Offered by Service Bureau,” Datamation, 13(1), February 1968, p. 69.; see also Goldstein, R. A. and Nagel, R., “3-D Visual Simulation”, Simulation, 16(1), pp.25-31, 1971] use ray casting for boolean set operations. Kay et al. [Kay, D. S. and Greenberg, D., “Transparency for Computer Synthesized Images,” SIGGRAPH'79, pp.158-164] and Whitted [“An Improved Illumination Model for Shaded Display”, CACM, 23(6), pp.343-349, 1980] use ray tracing for refraction and specular reflection computations. Airey et al. [Airey, J. M., Rohlf, J. H.. and Brooks, Jr. F. P., “Towards Image Realism with Interactive Update Rates in Complex Virtual Building Environments”, ACM SIGGRAPH Symposium on Interactive 3D Graphics, 24, 2(1990), pp. 41-50] uses ray casting for computing the portion of a model visible from a given cell. [0013] Another approach to visible-surface determination relies on sending beams or cones into a database of surfaces [see Dadoun et al., “Hierarchical approachs to hidden surface intersection testing”, Proceeedings of Graphics Interface '82, Toronto, May 1982, 49-56; see also Dadoun et al., “The geometry of beam tracing”, In Joseph O'Rourke, ed., Proceeedings of the Symposium on Computational Geometry, pp.55-61, ACM Press, New York, 1985]. Essentially, beams become a replacement for rays. The approach usually results in compact beams decomposing into a set of possibly non-connected cone(s) after interacting with an object. [0014] A variety of spatial subdivision schemes have been used to impose a spatial structure on the objects in a scene. The following four references pertain to spatial subdivision schemes: (a) Glassner, “Space subdivision for fast ray tracing,” IEEE CG&A, 4(10):15-22, October 1984; (b) Jevans et al., “Adaptive voxel subdivision for ray tracing,” Proceedings Graphics Interface '89, 164-172, June 1989; (c) Kaplan, M. “The use of spatial coherence in ray tracing,” in Techniques for Computer Graphics . . . , Rogers, D. and Earnshaw, R. A. (eds), Springer-Verlag, New York, 1987; and (d) Rubin, S. M. and Whitted, T. “A 3-dimensional representation for fast rendering of complex scenes,” Computer Graphics, 14(3):110-116, July 1980. [0015] Kay et al. [Kay, T. L. and Kajiya, J. T., “Ray Tracing Complex Scenes”, SIGGRAPH 1986, pp. 269-278,1986], concentrating on the computational aspect of ray casting, employed a hierarchy of spatial bounding volumes in conjunction with rays, to determine the visible objects along each ray. Of course, the spatial hierarchy needs to be precomputed. However, once in place, such a hierarchy facilitates a recursive computation for finding objects. If the environment is stationary, the same data-structure facilitates finding the visible object along any ray from any origin. [0016] Teller et al. [Teller, S. and Sequin, C. H., “Visibility Preprocessing for Interactive Walkthroughs,” SIGGRAPH '91, pp.61-69] use preprocessing to full advantage in visible-object computation by precomputing cell-to-cell visibility. Their approach is essentially an object precision approach and they report over 6 hours of preprocessing time to calculate 58 Mbytes of visibility information for a 250,000 polygon model on a 50 MIP machine [Teller, S. and Sequin. C. H., “Visibility computations in polyhedral three-dimensional environments,” U.C. Berkeley Report No. UCB/CSD 92/680, April 1992]. [0017] In a different approach to visibility computation, Greene et al. [Greene, N., Kass, M., and Miller, G., “Hierarchical z-Buffer Visibility,” SIGGRAPH '93, pp.231-238] use a variety of hierarchical data structures to help exploit the spatial structure inherent in object space (an octree of objects), the image structure inherent in pixels (a Z pyramid), and the temporal structure inherent in frame-by-frame rendering (a list of previously visible octree nodes). The Z-pyramid permits the rapid culling of large portions of the model by testing for visibility using a rapid scan conversion of the cubes in the octree. [0018] As used herein, the term “octree” refers to a data structure derived from a hierarchical subdivision of a three-dimensional space based on octants. The three-dimensional space may be divided into octants based on three mutually perpendicular partitioning planes. Each octant may be further partitioned into eight sub-octants based on three more partitioning planes. Each sub-octant may be partitioned into eight sub-suboctants, and so forth. Each octant, sub-octant, etc., may be assigned a node in the data structure. For more information concerning octrees, see pages 550-555, 559-560 and 695-698 of [0019] The depth complexity of graphical environments continues to increase in response to consumer demand for realism and performance. Thus, the efficiency of an algorithm for visible object determination has a direct impact on the marketability of a visualization system. The computational bandwidth required by the visible object determination algorithm determines the class of processor required for the visualization system, and thereby affects overall system cost. Thus, a system and method for improving the efficiency of visible object determination is greatly desired. [0020] Various embodiments of a system and method for performing visible object determination based upon a dual search of a cone hierarchy and a bounding hierarchy are herein disclosed. In one embodiment, the system may comprise a processor, a display device, system memory, and optionally a graphics accelerator. The processor executes visualization software which operates on a collection of graphics objects to determine a visible subset of the objects from a defined viewpoint. The objects may reside in a three-dimensional space and thus admit the possibility of occluding one another. [0021] The visualization software represents space in terms of a hierarchy of cones emanating from a viewpoint. In one embodiment, the leaf-cones of the cone hierarchy, i.e. the cones at the ultimate level of refinement, subtend an area which corresponds to a fraction of a pixel in screen area. For example, two cones may conveniently fill the area of a pixel. In other embodiments, a leaf-cone may subtend areas which include one or more pixels. [0022] An initial view frustum or neighborhood of the view frustum may be recursively tessellated (i.e. refined) to generate a cone hierarchy. Alternatively, the entire space around the viewpoint may be recursively tessellated to generate the cone hierarchy. In this embodiment, the cone hierarchy is recomputed for changes in the viewpoint and view-direction. The cone hierarchy is also referred to herein as the cone tree structure. [0023] The visualization software may also generate a hierarchy of bounds from the collection of objects. In particular, the bounding hierarchy may be generated by: (a) recursively grouping clusters starting with the objects themselves as order-zero clusters, (b) bounding each object and cluster (of all orders) with a corresponding bound, e.g. a polytope hull, (c) allocating a node in the bounding hierarchy for each object and cluster, and (d) organizing the nodes in the bounding hierarchy to reflect cluster membership. For example if node A is the parent of node B, the cluster corresponding to node A contains a subcluster (or object) corresponding to node B. Each node stores parameters which characterize the bound of the corresponding cluster or object. The bounding hierarchy is also referred to herein as the bound tree structure. [0024] The visualization software may perform a search of the cone tree structure and the bound tree structure starting with the root cone and the root bound. In one embodiment, each leaf-cone may be assigned a visibility distance value which represents the distance to the closest known object as perceived from within the leaf-cone. Each leaf-cone may also be assigned an object attribute which specifies the closest known object within view of the leaf-cone. Similarly, each non-leaf cone may be assigned a visibility distance value. However, the visibility distance value of a non-leaf cone is set equal to the maximum of the visibility distance values for its subcone children. This implies that the visibility distance value for each non-leaf cone equals the maximum of the visibility distance values of its leaf-cone descendents. [0025] In response to execution of the visualization software, the processor may perform a visibility search method which comprises: (a) receiving a cone pointer which points to the cone tree structure stored in a memory, (b) receiving a bound pointer which points to the bound tree structure stored in the memory, wherein leaf bounds of the bound tree structure approximate a collection of graphical objects, (c) searching the cone tree structure and the bound tree structure to determine one or more nearest graphical objects for one or more cones of the cone tree structure, and (d) transmitting an indication of the one or more nearest graphical objects for the one or more cones to a rendering agent such as a software renderer or a hardware rendering unit. The rendering agent generates pixel values (and/or samples) for the indicated objects. [0026] The dual-tree search may be illustrated in terms of a first cone of the cone tree structure and a first bound of the bound tree structure. The processor may compute a bound size for the first bound, and may compare the bound size to a cone size which corresponds to the first cone. If the bound size is larger than the cone size, the processor may search subbounds of the first bound with respect to the first cone. Conversely, if the cone size is larger than the bound size, the processor may search subcones of the first cone with respect to the first bound. A variety of methods are contemplated for computing the cone size and the bound size. By selecting the larger entity (first bound or first cone) for refinement, the visibility search method may, in some embodiments, prune the combined cone-bound tree more effectively, and determine the set of visible objects with increased efficiency. [0001] This application claims the benefit of U.S. Provisional Application No. 60/214,843 filed on Jun. 28, 2000 titled “Size Conditioned Visibility Search System and Method”. Citada por
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