US 20020050990 A1 Abstract A system and method for performing visible object determination based upon a dual search of a cone hierarchy and a bounding (e.g. hull) hierarchy. Visualization software running on a host processor represents space with a hierarchy of cones constructed by recursive refinement, and represents a collection of objects with a hierarchy of bounding hulls. 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 cone-restricted minimum distance between the cone and the hull is measured and compared to the visibility distance value of the cone. Only when the former is smaller than the latter will the cone be searched against the hull.
Claims(121) 1. A method for displaying visible objects on a display device, the method comprising:
searching (a) a bounding hierarchy generated from a collection of objects and (b) a cone hierarchy, to determine nearest objects for a subset of cones of the cone hierarchy; and displaying the nearest objects for the subset of cones of the cone hierarchy. 2. The method of 3. The method of determining a first measurement value of separation between the first cone and the first bound; determining whether the first measurement value satisfies an inequality condition with respect to a measurement value associated with the first cone; searching the first bound with respect to the first cone in response to an affirmative determination that the first measurement value satisfies the inequality condition with respect to the measurement value associated with the first cone. 4. The method of clustering said objects to form clusters;
bounding each object and each cluster with a corresponding bound;
allocating a node in the bounding hierarchy for each object and each cluster;
associating parameters with each node, wherein the parameters associated with the node describe the corresponding bound;
organizing the nodes so that node relationships represent cluster membership.
5. The method of 6. The method of 7. The method of 8. The method of 9. The method of 10. The method of 11. The method of 12. The method of 13. The method of 14. The method of 15. The method of 16. The method of 17. The method of 18. The method of 19. The method of 20. The method of 21. The method of 22. The method of 23. The method of 24. The method of 25. The method of 26. The method of 27. The method of determining whether or not said first cone is a leaf of the cone hierarchy and said first bound is a leaf of the bounding hierarchy; setting the measurement value associated with the first cone equal to the first measurement value of separation between the first bound and the first cone; setting a visible object attribute associated with the first cone equal to the first bound; wherein said setting of the measurement value associated with the first cone and said setting of the visible object attribute associated with the first cone are performed in response to an affirmative determination that the first cone is a leaf of the cone hierarchy and the first bound is a leaf of the hull hierarchy. 28. The method of determining whether said first cone is a leaf of the cone hierarchy and said first bound is not a leaf of the bounding hierarchy; conditionally exploring sub-bounds of said first bound with respect to said first cone in response to an affirmative determination that said first cone is a leaf of the cone hierarchy and said first bound is not a leaf of the bounding hierarchy. 29. The method of computing a cone-bound separation value for each of the sub-bounds of the first bound with respect to said first cone; conditionally searching said sub-bounds of said first bound with respect to said first cone in ascending order of separation from the first cone. 30. The method of determining whether the cone-bound separation value of a first subbound among said subbounds satisfies the inequality condition with respect to the measurement value associated with the first cone; searching said first sub-bound with respect to said first cone in response to an affirmative determination that said cone-bound separation value of said first sub-bound satisfies the inequality condition with respect to the measurement value associated with the first cone. 31. The method of determining if the first bound is a leaf of the bounding hierarchy and the first cone is not a leaf of the cone hierarchy; conditionally searching subcones of the first cone with respect to the first bound in response to an affirmative determination that said first bound is a leaf of the bounding hierarchy and said first cone is not a leaf of the cone hierarchy. 32. The method of computing a cone-bound separation value for the first bound with respect to a first subcone of the first cone; determining whether the cone-bound separation value satisfies the inequality condition with respect to a measurement value associated with the first subcone; searching said first subcone with respect to said first bound in response to an affirmative determination that said cone-bound separation value satisfies the inequality condition with respect to the measurement value associated with the first subcone. 33. The method of 34. The method of computing a first cone-bound separation value for the first bound with respect to a first subcone of the first cone; determining whether the first cone-bound separation value satisfies the inequality condition with respect to a measurement value associated with said first subcone; conditionally exploring subbounds of said first bound with respect to said first subcone in response to an affirmative determination that said first cone-bound separation value satisfies the inequality condition with respect to the measurement value associated with the first subcone. 35. The method of computing a second cone-bound separation value for each of said subbounds with respect to said first subcone; conditionally searching said subbounds of said first bound with respect to said first subcone in ascending order of their separation from the first subcone. 36. The method of determining whether the second cone-hull separation value for a first subbound among said subbounds satisfies the inequality condition with respect to the measurement value associated with the first subcone; searching said first subbound with respect to said first subcone in response to an affirmative determination that said second cone-hull separation value for the first subbound satisfies the inequality condition with respect to the measurement value associated with the first subcone. 37. The method of 38. The method of 39. The method of 40. The method of 41. A computer system for displaying visible objects, the computer system comprising:
a display device; a memory for storing a visibility search program; a processor coupled to the memory and configured to execute the visibility search program, wherein, in response to execution of the visibility search program, the processor is configured to search (a) a bounding hierarchy generated from a collection of objects and (b) a cone hierarchy, to determine nearest objects for a subset of cones of the cone hierarchy; wherein the display device is operable to display the nearest objects for the subset of cones in the cone hierarchy. 42. The computer system of 43. The computer system of determining a first measurement value of separation between a first cone of the cone hierarchy and a first bound of the bounding hierarchy; determining whether the first measurement value satisfies an inequality condition with respect to a measurement value associated with the first cone; searching the first bound with respect to the first cone in response to an affirmative determination that the first measurement value satisfies the inequality condition with respect to the measurement value associated with the first cone. 44. The computer system of 45. The computer system of clustering said objects to form clusters; bounding each object and each cluster with a corresponding bound; allocating a node in the bounding hierarchy for each object and each cluster; associating parameters with each node, wherein the parameters associated with each node describe the corresponding bound; and organizing the nodes so that node relationships represent cluster membership. 46. The computer system of 47. The computer system of 48. The computer system of 49. The computer system of 50. The computer system of 51. The computer system of 52. The computer system of 53. The computer system of 54. The computer system of 55. The computer system of 56. The computer system of 57. The computer system of 58. The computer system of 59. The computer system of 60. The computer system of 61. The computer system of 62. The computer system of 63. The computer system of 64. The computer system of 65. The computer system of 66. The computer system of 67. The computer system of 68. The computer system of determine whether or not said first cone is a leaf of the cone hierarchy and said first bound is a leaf of the bounding hierarchy; set the measurement value associated with the first cone equal to the first measurement value of separation between the first bound and the first cone; set a visible object attribute associated with the first cone equal to the first bound; wherein said setting of the measurement value associated with the first cone and said setting of the visible object attribute associated with the first cone are performed in response to an affirmative determination that the first cone is a leaf of the cone hierarchy and the first bound is a leaf of the hull hierarchy. 69. The computer system of determine whether said first cone is a leaf of the cone hierarchy and said first bound is not a leaf of the bounding hierarchy; conditionally explore sub-bounds of said first bound with respect to said first cone in response to an affirmative determination that said first cone is a leaf of the cone hierarchy and said first bound is not a leaf of the bounding hierarchy. 70. The computer system of compute a cone-bound separation value for each of the sub-bounds of the first bound with respect to said first cone; conditionally search said sub-bounds of said first bound with respect to said first cone in ascending order of separation from the first cone. 71. The computer system of determine whether the cone-bound separation value of a first subbound among said subbounds satisfies the inequality condition with respect to the measurement value associated with the first cone; search said first sub-bound with respect to said first cone in response to an affirmative determination that said cone-bound separation value of said first sub-bound satisfies the inequality condition with respect to the measurement value associated with the first cone. 72. The computer system of determine if the first bound is a leaf of the bounding hierarchy and the first cone is not a leaf of the cone hierarchy; conditionally search subcones of the first cone with respect to the first bound in response to an affirmative determination that said first bound is a leaf of the bounding hierarchy and said first cone is not a leaf of the cone hierarchy. 73. The computer system of computing a cone-bound separation value for the first bound with respect to a first subcone of the first cone; determining whether the cone-bound separation value satisfies the inequality condition with respect to a measurement value associated with the first subcone; searching said first subcone with respect to said first bound in response to an affirmative determination that said cone-bound separation value satisfies the inequality condition with respect to the measurement value associated with the first subcone. 74. The computer system of 75. The computer system of compute a first cone-bound separation value for the first bound with respect to a first subcone of the first cone; determine whether the first cone-bound separation value satisfies the inequality condition with respect to a measurement value associated with said first subcone; conditionally explore subbounds of said first bound with respect to said first subcone in response to an affirmative determination that said first cone-bound separation value satisfies the inequality condition with respect to the measurement value associated with the first subcone. 76. The computer system of compute a second cone-bound separation value for each of said subbounds with respect to said first subcone; conditionally search said subbounds of said first bound with respect to said first subcone in ascending order of their separation from the first subcone. 77. The computer system of determine whether the second cone-hull separation value for a first subbound among said subbounds satisfies the inequality condition with respect to the measurement value associated with the first subcone; search said first subbound with respect to said first subcone in response to an affirmative determination that said second cone-hull separation value for the first subbound satisfies the inequality condition with respect to the measurement value associated with the first subcone. 78. The computer system of 79. The computer system of 80. The computer system of 81. The computer system of 82. A memory media which stores program instructions for determining visible objects for display on a display device, wherein the program instructions are executable by a processor to implement:
searching (a) a bounding hierarchy generated from a collection of objects and (b) a cone hierarchy, to determine nearest objects for a subset of cones of the cone hierarchy; displaying the nearest objects for the subset of cones of the cone hierarchy. 83. The memory media of 84. The memory media of determining a first measurement value of separation between the first cone and the first bound; determining whether the first measurement value satisfies an inequality condition with respect to a measurement value associated with the first cone; searching the first bound with respect to the first cone in response to an affirmative determination that the first measurement value satisfies the inequality condition with respect to the measurement value associated with the first cone. 85. The memory media of clustering said objects to form clusters; bounding each object and each cluster with a corresponding bound; allocating a node in the bounding hierarchy for each object and each cluster; associating parameters with each node, wherein the parameters associated with the node describe the corresponding bound; organizing the nodes so that node relationships represent cluster membership. 86. The memory media of 87. The memory media of 88. The memory media of 89. The memory media of 90. The memory media of 91. The memory media of 92. The memory media of 93. The memory media of 94. The memory media of 95. The memory media of 96. The memory media of 97. The memory media of 98. The memory media of 99. The memory media of 100. The memory media of 101. The memory media of 102. The memory media of 103. The memory media of 104. The memory media of 105. The memory media of 106. The memory media of 107. The memory media of 108. The memory media of determining whether or not said first cone is a leaf of the cone hierarchy and said first bound is a leaf of the bounding hierarchy; setting the measurement value associated with the first cone equal to the first measurement value of separation between the first bound and the first cone; setting a visible object attribute associated with the first cone equal to the first bound; wherein said setting of the measurement value associated with the first cone and said setting of the visible object attribute associated with the first cone are performed in response to an affirmative determination that the first cone is a leaf of the cone hierarchy and the first bound is a leaf of the hull hierarchy. 109. The memory media of determining whether said first cone is a leaf of the cone hierarchy and said first bound is not a leaf of the bounding hierarchy; conditionally exploring sub-bounds of said first bound with respect to said first cone in response to an affirmative determination that said first cone is a leaf of the cone hierarchy and said first bound is not a leaf of the bounding hierarchy. 110. The memory media of computing a cone-bound separation value for each of the sub-bounds of the first bound with respect to said first cone; conditionally searching said sub-bounds of said first bound with respect to said first cone in ascending order of separation from the first cone. 111. The memory media of determining whether the cone-bound separation value of a first subbound among said subbounds satisfies the inequality condition with respect to the measurement value associated with the first cone; searching said first sub-bound with respect to said first cone in response to an affirmative determination that said cone-bound separation value of said first sub-bound satisfies the inequality condition with respect to the measurement value associated with the first cone. 112. The memory media of determining if the first bound is a leaf of the bounding hierarchy and the first cone is not a leaf of the cone hierarchy; conditionally searching subcones of the first cone with respect to the first bound in response to an affirmative determination that said first bound is a leaf of the bounding hierarchy and said first cone is not a leaf of the cone hierarchy. 113. The memory media of computing a cone-bound separation value for the first bound with respect to a first subcone of the first cone; p 1 determining whether the cone-bound separation value satisfies the inequality condition with respect to a measurement value associated with the first subcone; searching said first subcone with respect to said first bound in response to an affirmative determination that said cone-bound separation value satisfies the inequality condition with respect to the measurement value associated with the first subcone. 114. The memory media of 115. The memory media of computing a first cone-bound separation value for the first bound with respect to a first subcone of the first cone; determining whether the first cone-bound separation value satisfies the inequality condition with respect to a measurement value associated with said first subcone; conditionally exploring subbounds of said first bound with respect to said first subcone in response to an affirmative determination that said first cone-bound separation value satisfies the inequality condition with respect to the measurement value associated with the first subcone. 116. The memory media of computing a second cone-bound separation value for each of said subbounds with respect to said first subcone; conditionally searching said subbounds of said first bound with respect to said first subcone in ascending order of their separation from the first subcone. 117. The memory media of determining whether the second cone-hull separation value for a first subbound among said subbounds satisfies the inequality condition with respect to the measurement value associated with the first subcone; searching said first subbound with respect to said first subcone in response to an affirmative determination that said second cone-hull separation value for the first subbound satisfies the inequality condition with respect to the measurement value associated with the first subcone. 118. The memory media of 119. The memory media of 120. The memory media of 121. The memory media of Description [0001] This application is a continuation of U.S. patent application Ser. No. 09/247,466 filed on Feb. 09, 1999, U.S. Pat. No. 6,300,965 entitled “Visible-Object Determination for Interactive Visualization” which claims the benefit of U.S. provisional application Ser. No. 60/074,868 filed on Feb. 17, 1998 entitled “Visible-Object Determination for Interactive Visualization”. [0002] The present invention relates generally to the field of computer graphics, and more particularly, to the problem of determining the set of objects/surfaces visible from a defined viewpoint in a graphics environment. [0003] 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. [0004] A hardware pipeline wastes rendering bandwidth when it discards 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 examples, software frustum culling can significantly reduce discards in a pipeline's clipping computation while software backface culling can reduce discards in a pipeline's lighting computation. [0005] 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. 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. [0006] Software occlusion culling has been proposed as an additional tool for improving rendering effectiveness. A visualization program which performs occlusion culling effectively increases the graphic hardware's overall rendering bandwidth 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 even a frustum culled scene, a program instead computes which objects are visible and draws just those. A simple visualization program can compute the set of visible objects and draw those objects from the current viewpoint, allowing the pipeline to remove backfacing polygons and the z-buffer to remove any non-visible surfaces. [0007] One technique for computing the visible object set uses ray casting. RealEyes [Sowizral, H. A., Zikan, K., Esposito, C., Janin, A., Mizell, D. “ [0008] 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 denoted A is small enough to be entirely missed by the given ray sample. [0009] Visible-object determination has its roots in visible-surface determination. Foley et al. [Foley, J., van Dam, A., Feiner, S. and Hughes, J. [0010] 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,” [0011] 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.” [0012] 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 [0013] 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. [0014] 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 ]. [0015] 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. [0016] 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 effects overall system cost. Thus, a system or method for improving the efficiency of visible object determination is greatly desired. [0017] The present invention comprises a system and method for displaying visible objects in a graphics environment. In particular, a system and method for performing visible object determination based upon a dual search of a cone hierarchy and a bounding hierarchy is herein disclosed. The system includes a processor, a display device, system memory, and optionally a graphics accelerator. The processor executes visualization software which provides for visualization of a collection of three-dimensional objects on the display device. The objects reside in a three-dimensional space and thus admit the possibility of occluding one another. [0018] The visualization software represents space in terms of a hierarchy of cones emanating from the viewpoint. In one embodiment, the leaf-cones of the hierarchy, i.e. the cones at the highest 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. Alternatively, the leaf-cone may subtend areas which include one or more pixels. [0019] An initial view frustum or neighborhood of the view frustum is 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 case, the cone hierarchy does not need to be recomputed for changes in the viewpoint and view-direction. [0020] The visualization software also generates a hierarchy of bounds from the collection of objects. In particular, the bounding hierarchy is 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. [0021] The visualization software performs a search of the cone and bounding hierarchies starting with the root cone and the root bound. Each leaf-cone is assigned a visibility distance value which represents the distance to the closest known object as perceived from within the leaf-cone. Each leaf-cone is also assigned an object attribute which specifies the closest known object within view of the leaf-cone. Similarly, each non-leaf cone is 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. [0022] The visibility software operates on cone-bound pairs. Before exploring a given cone-bound pair, the distance between the cone and the bound is measured. This involves determining the minimum distance to points residing in both the bound and the cone from the vertex of the cone. This cone-bound distance is then compared to the visibility distance value of the cone. If the cone-bound distance is larger than the visibility distance value of the cone, all of the leaf-cone descendents of the given cone have known visible objects closer than the given bound by definition of the visibility distance value. Thus, no benefit can be gained from exploring the cone-bound pair. In contrast, if the cone-bound distance is smaller than the visibility distance value of the cone, the bound may contain objects which will affect the visibility distance values of one or more leaf-cone descendents of the given cone. The cone-bound pair must be searched. According to the present invention, cone-bound pairs are advantageously searched only when there is a possibility that the given bound may affect the visibility of the cone's descendents. Thus, the search algorithm of the present invention avoids unnecessary cone-bound explorations and thereby saves considerable computational bandwidth. [0023] Supposing that the search condition is satisfied, the bound is explored with respect to the given cone. If the cone and bound are both leaves of their respective hierarchies, the bound specifies an object which is closer than the closest known object for the leaf-cone. Thus, the visibility distance value of the leaf-cone is updated with the cone-bound distance between the cone and bound. Also, the object attribute for the cone is updated to point to the given bound. [0024] In the case that the cone is a leaf-cone and the bound is a non-leaf bound, the search algorithm examines subbounds of the given bound, and conditionally explores these subbounds in ascending order of their cone-bound distance from the given cone. Again, exploration of a subbound is conditioned upon the subbound achieving a cone-bound distance to the given cone which is smaller than the cone's visibility distance value. [0025] In the case that the cone is a non-leaf cone and the bound is a leaf bound (i.e. one which bounds a single object), the search algorithm conditionally explores subcones of the given cone with respect to the given bound. Exploration of a subcone is conditioned upon the subcone achieving a cone-bound distance to the given bound which is smaller than the subcone's visibility distance value. [0026] In the case that the cone is a non-leaf cone and the bound is a non-leaf bound, the search algorithm conditionally explores subbounds of the given bound against the subcones of the given cone. Consider a particular subcone of the given cone for the sake of discussion. The subbounds of the given bound are conditionally explored against the subcone in ascending order of their cone-bound distances from the subcone. Because the closest subbound is searched first, and potentially decreases the visibility distance value of the given subcone, succeeding (more distant) subbounds will have more difficulty passing the search condition, i.e. of having a cone-bound distance to the given subcone which is less than the visibility distance value of the subcone. Thus, the probability is maximized that the fewest number of subbounds will need to be explored by ordering the conditional explorations according to cone-bound distance. [0027] When the search of the two trees is completed, the object attribute of each leaf-cone points to the object which is visible to the leaf-cone, and the visibility distance value of the leaf-cone specifies the distance to the visible object. This visibility information is provided to the graphics accelerator so that the graphics accelerator may render the visible objects (or visible portions of visible object) on the display device. [0028] In one embodiment, the visualization software provides for interactive visualization by reading user inputs to control the current viewpoint and view-direction in the graphics environment. Additional software ensures efficient computation through the use of careful state management and parallelism. [0029] In one alternative embodiment, the cone hierarchy and bounding hierarchy are searched iteratively. In a second alternative embodiment, a level order search is performed on the cone hierarchy and the bounding hierarchy. [0030] The present invention contemplates a wide variety of techniques for measuring the extent of separation or proximity between a bound and a cone. One set of embodiments focus of minimizing an increasing function of separation distance between the vertex of the cone and points in the intersection of the cone and the bound. Another set of embodiments involve 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 general, any wavefront with a boundary that obeys a mild “star shape” condition may provide the basis for a measurement of separation between a bound and a cone. [0031]FIG. 1 illustrates the ray-based method of visible object detection according to the prior art; [0032]FIG. 2A illustrates a graphical computing system according to the present invention; [0033]FIG. 2B is a block diagram illustrating one embodiment of the graphical computing system of the present invention; [0034]FIG. 3 illustrates several main phases of visualization software according to the present invention; [0035]FIG. 4A illustrates a collection of objects in a graphics environment; [0036]FIG. 4B illustrates a first step in the first formation of a hull hierarchy, i.e. the step of bounding objects with containing hulls and allocating hull nodes for the containing hulls; [0037]FIG. 4C illustrates 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; [0038]FIG. 4D illustrates the culmination 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; [0039]FIG. 5A illustrates the mathematical expressions which describe lines and half-planes in two dimensional space; [0040]FIG. 5B illustrates the description of a rectangular region as the intersection of four half-planes in a two dimensional space; [0041]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; [0042]FIG. 7 illustrates polyhedral cones with rectangular and triangular cross-section emanating from the origin; [0043]FIG. 8A illustrates the mathematical expressions which describe a line through the origin and a corresponding half-plane given a normal vector in two-dimensional space; [0044]FIG. 8B illustrates the specification of a two-dimensional conic region as the intersection of two half-planes; [0045] FIGS. [0046]FIG. 10 illustrates a visibility search algorithm according to the present invention; [0047]FIG. 11 illustrates a method for displaying visible objects in a graphics environment; [0048]FIG. 12 illustrates the process of recursively clustering a collection of objects to form a bounding hierarchy; [0049]FIG. 13 illustrates the processing steps to be performed when the visibility search arrives at a terminal cone and a terminal bound; [0050]FIG. 14 illustrates the processing steps to be performed when the visibility search arrives at a terminal cone and a non-terminal bound; [0051]FIG. 15 further elaborates step [0052]FIG. 16 further elaborates step [0053]FIG. 17 illustrates the processing steps to be performed when the visibility search arrives at a terminal bound and a non-terminal cone; [0054]FIG. 18 further elaborates step [0055]FIG. 19 illustrates the processing steps to be performed when the visibility search arrives at a non-terminal bound and a non-terminal cone; [0056]FIG. 20 further elaborates step [0057]FIG. 21 further elaborates step [0058] U.S. patent application Ser. No. 09/247,466 filed on Feb. 9, 1999 entitled “Visible-Object Determination for Interactive Visualization” is hereby incorporated by reference in its entirety. [0059]FIG. 2A presents a graphical computing system [0060]FIG. 2B is a block diagram illustrating one embodiment of graphical computing system [0061] An optional 3-D graphics accelerator [0062] 3-D graphics accelerator [0063] The computer system [0064] Visualization Software Architecture [0065] As illustrated in FIG. 3, the visualization software of the present invention comprises three main phases. In an initial step [0066] In one embodiment of the visualization software, the viewpoint in the graphical environment may be changed by user input. For example, by manipulating the mouse [0067] In step [0068] Unless otherwise stated, it is assumed that all objects in the model are 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 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. The convexity assumption presents more of a problem. However, 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 quire an exact computation, but rather a conservative one. In other words, it is permissible to over-estimate the set of visible objects. [0069] Constructing the Object Hierarchy [0070] Initially, the objects in the scene are 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. [0071] Each object is 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 [0072] 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 of a vector v with the unit vector n, denoted v·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 distance |c| along line L in the direction opposite to unit vector n. [0073] 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. [0074]FIG. 5B shows how a rectangular region may be defined as the intersection of four half-planes. Given four normal vectors n [0075] In three-dimensional space, a rectangular box may be analogously defined as the intersection of six half-spaces. Given six normal vectors n [0076] To construct an object hierarchy, object hulls H [0077] The containing-hulls H [0078] 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 us with 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. [0079] 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 the preferred embodiment of the invention, the hull hierarchy comprises a fixed-direction collection of hulls. [0080] 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. [0081] In a second embodiment, eight normal vectors directed towards the coners 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. [0082] 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 coners 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. [0083] 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 coners 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. [0084] 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. This may be particularly advantageous for scenes which have non-uniform object density. For example, if a large number of small objects are clustered in a scene, it may be advantageous to include these in a single group, i.e. bound them with a single containing hull. Larger objects may be assembled into groups with fewer members. [0085] 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. 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. [0086] Cones in Visible Object Determination [0087] In addition to the bounding hierarchy (e.g. hull hierarchy) discussed above, the present invention makes use of a hierarchy of spatial cones. An initial cone which represents 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. This is partially due to the fact that the ray-casting methods require multiple unrelated visibility queries to cover a region equivalent to a cone. [0088] 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. [0089]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 [0090] 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 [0091] Polyhedral Cones [0092] The spatial cones used in the preferred embodiment are polyhedral cones. The generic polyhedral cone has a polygonal cross-section. FIG. 7 give two examples of polyhedral cones. The first polyhedral cone PC [0093] A polyhedral cone is constructed by intersection of multiple half-spaces. For example, solid cone PC [0094] 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 considers 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. [0095] Distance Measurement [0096] In view of the discussion concerning wave propagation, the distance of a 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
[0097] where ∥x∥ is the norm of vector x. When the object, hull, or bound X is empty, the distance is taken to be positive infinity. [0098] 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: the entire boundary of the wavefront must be 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 [0099] Cones and Visibility [0100] 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. [0101] 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
[0102] If the distance f [0103] 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 generates the value of the corresponding pixel. [0104] 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 of the present invention may advantageously detect small visible objects or portions of objects that would be missed by a ray-based method with equal angular resolution. [0105] Generalized Separation Measurement [0106] 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 must satisfy a mild “star shape” condition: the entire boundary of the wavefront must be visible from the vertex of the cone. [0107] 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. [0108] 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 separation 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. [0109] A Cone Hierarchy [0110] The visibility determination method of the present invention relies on the use of 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 very 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. [0111] 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. [0112] FIGS. [0113] The triangular hierarchical decomposition shown in FIGS. [0114] 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. [0115] Discovering the Set of Visible Objects [0116] Once the hull hierarchy and the cone hierarchy have been constructed, the set of visible objects is computed from the current viewpoint. In one embodiment, the visible object set is repeatedly recomputed for a succession of viewpoints and viewing directions. The successive viewpoints and viewing directions may be specified by a user through an input device such as a mouse, joystick, keyboard, trackball, or any combination thereof. The visible object determination method of the present invention is organized as a simultaneous search of the hull tree and the cone tree. The search process involves 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. [0117] Viewing the Scene [0118] 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. [0119] Managing the Visible-Object-Set [0120] The visualization software must 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 must therefore 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. [0121] Computing Visibility Using Cones [0122] Substantial computation 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. [0123] Visibility Search Algorithm [0124] In the preferred embodiment, the visibility search algorithm of the present invention is realized in a visibility search program stored in memory [0125] 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
[0126] 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 [0127] The recursive search starts with the root H of the hull tree and the root 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 is required. Either tree may be refined at this point. In the preferred embodiment, both trees are refined in a predefined order. [0128] The pruning mechanism is built upon several basic elements. A distance measurement function computes the distance f [0129] 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. 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. [An object-hull is a hull that directly bounds an object. Thus, object-hulls are terminal nodes of the hull tree.] In addition to a visibility distance value, each leaf-cone node is assigned storage for an currently visible object. This object attribute is initialized to NO_OBJECT or BACKGROUND depending upon the scene context. [0130] 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 its sub-cones. Or equivalently, the extent value for a non-leaf cone equals the maximum 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 H achieve a cone-object distance f [0131] The following code fragment illustrates the beginning of the search process. The variables hullTree and coneTree point to the root nodes of the hull tree and cone tree respectively.
[0132] The DIST function 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. [0133] FIGS. findVisible ( H, C, d) [0134] where H is a hull node to be explored against the cone node C. The value d represents the cone-hull distance between cone C and hull H. In step [0135] If the hull H and cone C are not both leaves, step [0136] In step [0137] In step [0138] It is noted that the call to the function findVisible in step [0139] In step [0140] If, in step [0141] In step [0142] Since the visibility distance values for subcones C [0143] If, in step [0144] In step [0145] In step [0146] If, in step [0147] Since the closer subhull H [0148] Step [0149] If, in step [0150] In step [0151] As explained above, the visibility distance value assigned to each cone in the cone tree equals the maximum of the visibility distance values assigned to its subcone children. Thus, if a given hull node achieves a distance to a cone which is larger than the cone's current visibility distance value, it is immediately apparent that none of the hulls descendents will be of interest to any of the cone's descendents. The given hull node may be skipped so far this cone is concerned. [0152] 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 an exact-computation for the visibility distance values. [0153] Throughout the above discussion of the visibility search algorithm it has been assumed that the DIST function 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 DIST function 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. [0154] In general, the DIST function 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 DIST function uses an increasing or decreasing function of separation. [0155] 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. [0156] Method for Displaying Visible Objects [0157] A method for displaying visible objects in a graphics environment is described in the flowchart of FIG. 11. A visibility search algorithm executing on CPU [0158] In step [0159] The search procedure performs conditional explorations of cone-bound pairs starting with the root cone of the cone hierarchy and the root bound of the bounding hierarchy. Given a first cone in the cone hierarchy and a first bound in the bounding hierarchy, the search algorithm determines a first measurement value which indicates an extent of separation between the first cone and the first bound as shown in step [0160] In step [0161] In step [0162] In one embodiment, the first measurement value comprises a penalty of separation between the first cone and the first bound which is determined by minimizing an increasing function of separation distance between the vertex of the first cone and points in the intersection of the first cone and the first bound. In this case, step [0163] In the preferred embodiment, the first measurement value of separation between the first bound and the first cone is computed by solving a linear programming problem using the linear constraints given by normal matrix S for the first cone, and the linear constraints given by the normal matrix N and the extent vector c for the first bound. Recall the discussion in connection with FIGS. [0164] In an alternate embodiment, the first measurement value comprises a merit of proximity (i.e. closeness) between the first cone and the first bound which is determined by maximizing a decreasing function of separation distance between the vertex of the first cone and points in the intersection of the first cone and the first bound. In this case, step [0165]FIG. 12 illustrates step [0166] In step [0167] 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. [0168] A Terminal Cone-Bound Pair [0169]FIG. 13 illustrates a first portion of step [0170] A Terminal Cone with a Non-Terminal Bound [0171]FIG. 14 illustrates a second portion of step [0172]FIG. 15 illustrates step [0173]FIG. 16 illustrates step [0174] In step [0175] A Terminal Hull with a Non-Terminal Cone [0176]FIG. 17 illustrates a third portion of step [0177]FIG. 18 illustrates step [0178] In step [0179] After the subcones of the first cone have been searched against the first bound, the measurement value associated with the first cone is updated. Namely, the measurement value associated with the first cone may be set equal to an extremum (i.e. maximum or minimum) of the measurement values associated with the subcones of the first cone. 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. [0180] A Non-Terminal Cone with a Non-Terminal Bound [0181]FIG. 19 illustrates a fourth portion of step [0182]FIG. 20 illustrates step [0183]FIG. 21 illustrates step [0184] In step [0185] After the subbounds of the first bound have been searched against the first subcone, a second subcone of the first cone is tested. In particular, the visibility search algorithm computes a measure of the separation of the first bound from the second subcone. If this measure of separation satisfies an inequality condition with respect to a measurement value associated with the second subcone, then subbounds of the first bound may be conditionally explored with respect to the second subcone. The conditional exploration of the second subcone is similar to the conditonal exploration of the first subcone described in conjunction with FIGS. 20 and 21. [0186] After all subcones of the first cone have been tested and conditionally searched as described above, the measurement value associated with the first cone is set equal to an extremum of the measurement values associated with the subcones. As noted above, the maximum is chosen as the extremum in embodiments which use an increasing function of separation to compute cone-bound separation values, and the minimum is chosen in embodiments which use a decreasing function of separation to compute the cone-bound separation values. [0187] In one alternate embodiment, 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. [0188] Although the search of the bounding hierarchy and the cone hierarchy 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 hierarchy and cone hierarchy. [0189] Computing the Cone Restricted Distance Function [0190] Recall that evaluation of the cone-hull distance f [0191] It is also noted that cone-hull separation may be measured by maximizing an increasing function separation such as ∥x∥ [0192] 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 must 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. [0193] A piecewise-linear formulation of distance f min(v [0194] The vector v is some member of the cone that is polar to the cone C. For instance, V=−S max(b [0195] The dual objective value, b [0196] In the preferred embodiment, the bounding hulls have sides normal to a fixed set of normal vectors. Thus, the matrix A {( [0197] 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. [0198] To establish the setting, the orientation of the cone hierarchy needs to be fixed. This is not feasible if the cone hierarchy changes orientation with changes in orientation of the view frustum. Thus, the entire space is tessellated with cones, and visible objects are detected within the entire space. Only after this entire space computation is the set of visible of objects culled to conform to the current view frustum. [0199] In an alternative embodiment, a less aggressive approach may be pursued. Namely, by noting which subsets of the cones correspond to the current orientation of the view frustum, only this subset may be included in the visible-object-set computation. [0200] Memory Media [0201] As described above, the visibility software and visibility search program of the present invention are preferably stored in memory [0202] Multiple Objects Per Cone [0203] According to the visibility search algorithm, a single nearest object is identified for each leaf cone (i.e. terminal cone). If each of the leaf cones have the ultimate resolution, i.e. the resolution of a pixel, then the strategy of identifying the nearest object in each leaf cone is guaranteed to detect all visible objects. However, the visibility search of the fully resolved cone hierarchy 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, there is 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. [0204] In order to increase the probability of capturing the full set of visible objects, the visibility search algorithm may be modified to 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 cone hierarchy is close to ultimate resolution K may be close to one. Conversely, if the cone hierarchy is poorly resolved, i.e. includes only a few levels of refinement, the integer K may be larger. [0205] The present invention contemplates maximizing computational efficiency along the axis of high-cone-resolution/low-K-value on the one hand and low-cone-resolution/high-K-value on the other. [0206] Adaptive Refinement of the Cone Hierarchy [0207] 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. [0208] 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. [0209] In general the cones of the cone hierarchy may be at differing levels of refinement. Cone refinement is allowed only if the cone interacts with an object or bound (e.g. hull). Adaptive refinement of a cone terminates when the cone resolution equals that of a pixel or when no object occurs in the cone. [0210] It is noted that a combination of fixed refinement and adaptive refinement of the cone hierarchy may be used. [0211] The present invention also contemplates adaptively refining the cone hierarchy and identifying the K nearest objects/bound for each cone, where K changes the refinement level changes. [0212] Non-Occluding Objects [0213] 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. The present invention contemplates certain modifications to the visibility search algorithm to 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 Referenced by
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