Efficient collision detection using bounding volume hierarchies of k-DOPs by James T. Klosowski,...
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Efficient collision detection using bounding volume hierarchies of k-DOPs
byJames T. Klosowski,
Martin Held, Joseph S.B. Mitchell,Henry Sowizral, and Karel Zikan
Presented by Paul Phipps
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Overview
• Background• Collision Detection Perspective• k-DOP• Cost Function• Design Choices for BV Tree• Tumbling the k-DOPs• Experimental Results• Future Work
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Background• Collision Detection
– Pure detection– Detect and report
• Do fewer comparisons– Between pairs of objects– Between pairs of primitives
• Approaches– Spatial Decomposition
• Octrees, k-d trees, BSP-trees, brep-indices, tetrahedral meshes, and (regular) grids
– Bounding Volumes Hierarchy• Spheres, axis-aligned bounding boxes (AABBs), oriented
bounding box (OBB) (“RAPID” uses OBBTrees)– Miscellaneous
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Collision Detection Perspective• Assumptions
– Rigid bodies– Discrete points in time
• Typical input:– Static object (the environment)– Moving object (the flying hierarchy)
• Goals:– Accuracy– Real-time rates
• Haptics can require over 1000 collision queries per second
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Contributions…
• “k-DOP” (Discrete Orientation Polytope)– convex polytope whose facets are determined
by halfspaces whose outward normals come from a small fixed set of k orientations
– Axis Aligned Bounding Box == 6-DOP– ((using axes +x, -x, +y, -y, +z, and -z)
• k-DOPs used in experiments:– 6-DOP, 14-DOP, 18-DOP, 26-DOP
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…Contributions• Compare ways to construct a Bounding
Volume Hierarchy (“BV-tree”) of k-DOPs
• Algorithms– Maintain k-DOP BV-tree for moving objects
• Translation• Rotation
– Fast collision detection• Using BV-trees of moving object and of
environment
• Results with real and simulated data
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Cost Function
• N = number of occurrences• C = cost per occurrence
uuppvv CNCNCNT
Bounding Volume Overlap Tests
Primitive Overlap Tests
Updates of Hierarchy nodes
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k-DOP Advantages over other BV
• Tighter fit than AABB or Sphere– The higher the k the lower Nv, Np, and Nu
• Only k values to remember for a BV (using opposite-pointing orientations)
• Simpler overlap tests than OBB– Just do (k / 2) interval overlap tests
• The parameter k can be chosen to get a good balance between tight fit and quick overlap test
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Design Choices for BV Tree
• Branching degree 2 (binary tree) is good– Simple to implement– Simple to traverse tree
• Splitting rule for pre-computing the tree structure– Pick either x, y, or z axis (using various tests)– Sort along that axis, then use
• Median• Mean
– Recur
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Tumbling the k-DOPs
• “hill climbing” method– Use pre-computed convex hulls– Are extreme vertices still extreme?
• If not, “climb” to more extreme neighbors
– Advantage: tight
• “approximation method”– Rotate vertices of k-DOP
• Get new k-DOP• Don’t accumulate error: Rotate from pre-computed vertices in
Model-Space
– Advantage: fast
For Root Node
For non-Root Nodes
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Algorithm 1
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Future Work
• Use of temporal coherence
• Multiple flying objects
• Dynamic environments
• Deformable objects
• Numerically Controlled Verification
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Overview
• Background• Collision Detection Perspective• k-DOP• Cost Function• Design Choices for BV Tree• Tumbling the k-DOPs• Experimental Results• Future Work
![Page 24: Efficient collision detection using bounding volume hierarchies of k-DOPs by James T. Klosowski, Martin Held, Joseph S.B. Mitchell, Henry Sowizral, and.](https://reader035.fdocuments.in/reader035/viewer/2022062519/5697c0211a28abf838cd3065/html5/thumbnails/24.jpg)
Questions?
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Continuous Collision Detectionof Deformable Objects using k-DOPs