The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Collision Detection for Deformable Objects Xin Huang 16/10/2007

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 2 Application

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 3 Overview Deformable models  deforming over time, cutting, breaking …..  expensive to update the collision query structure such as BVH as model deforming

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 4 Overview Self- collision  many adjacent or close primitives overlap  result in a high number of false positives  very challenging

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 5 Outline [Larsson 01] Collision detection for continuously deforming bodies [Zhang 2007] Interactive Collision Detection for Deformable Models [Govindaraju 03] CULLIDE: Interactive Collision Detection between Complex Models in Large Environments using Graphics Hardware [Govindaraju 05] Interactive Collision Detection between Deformable Models using Chromatic Decomposition

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 6 [Larsson 01] Collision Detection for Continuously Deforming Bodies [Larsson 01]  hybrid update: an incremental bottom-up and a selective top-down update

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 7 [Larsson 01] Broad phase: sort and prune Narrow phase: update, AABBs test, primitive test

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 8 [Larsson 01] Bounding volume pre-processing  Built 8-ary AABB tree in top-down manner  A parent AABB is split along three principle axis to form 8 child sub-volumes  Split planes: center point of the box or average of all polygon’s midpoints

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 9 [Larsson 01] Run-time AABB Updates  Top-down: traversing the faces under the node; benefit if a few deep nodes  Bottom-up: Directly from AABBs of sub- node; benefit if many deep nodes  Trade-off

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 10 [Larsson 01] Hybrid update  For a tree with depth n, initially update the n / 2 first levels bottom-up  Non-updated nodes are updated top-down on the fly during collision traversal

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 11 [Larsson 01] (a)reporting all intersecting triangle pairs (b) first arbitrary intersecting triangle pair

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 12 [Larsson 01] Conclusion  Fast to update during deformation  Not consider Self-collision

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 13 [Zhang 2007] Interactive Collision Detection for Deformable Models using Streaming AABBs---[Zhang 2007]  Bound deformable objects as input streams  Use GPU to perform parallel pairwise overlap test  Compute in object space Previous GPU based method  Depend on image resolution and view direction

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 14 [Zhang 2007]

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 15 [Zhang 2007] AABB tree building & texture preparation

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 16 [Zhang 2007] Streaming AABB Overlap Tests

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 17 [Zhang 2007] Hierarchical Readback

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 18 [Zhang 2007] Primitive Intersection Test: on CPU Stream Update: on GPU

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 19 Examples [Zhang 2007]

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 20 [Zhang 2007] Excluding texture download from CPU to GPU, 2-10 times faster Including texture download, times faster

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 21 [Zhang 2007]

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 22 [Zhang 2007] Conclusion  Streaming AABB overlap tests and stream update using SIMD computations  Stream reduction readback  Collision detection in Object space

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 23 [Zhang 2007] Limitation  Pre-setup time to prepare AABB streams and map to textures in GPU’s memory  Need more texture memory  Not report self-intersections

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 24 CULLIDE---[ Govindaraju 03 ] CULLIDE: Interactive Collision Detection Between Complex Models in Large Environments using Graphics Hardware Compute potentially colliding set (PCS) Visibility query by graphics hardware

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 25 CULLIDE---[ Govindaraju 03 ] An object O does not collide with a set of objects S if O is fully-visible with respect to S. Compute PCS by two pass  1st pass, render the objects in the order O1,..,On  2nd pass, render the objects in the reverse order On,On−1,...O1  test if an object is fully visible or not, if not, in PCS

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 26 CULLIDE---[ Govindaraju 03 ] Object level pruning  perform object level pruning by computing the PCS of objects. Sub-Object Pruning  identify potential regions of each object in PCS Exact Collision Detection  Triangle-triangle intersection on the CPU

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 27 CULLIDE---[Govindaraju 03]

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 28 CULLIDE---[ Govindaraju 03 ] Limitation  No distance and penetration information  Image-space resolution  Cannot handle self-collision

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 29 Self-collision Challenge  Non-interactive rates  Many adjacent or nearby primitives in close proximity  A high number of false positives

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 30 [Govindaraju 05] Interactive Collision Detection between Deformable Models using Chromatic Decomposition Chromatic Mesh Decomposition Set-based Self-Collision Detection

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 31 [Govindaraju 05] Non-Adjacent Collision Detection (NACD): AABB and 2.5D overlap test Adjacent Collision Detection (ACD): exact VF and EE elementary tests

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 32 [Govindaraju 05] Chromatic Mesh Decomposition  Independent Sets  Graph Coloring  Construct an extended-dual graph G = (V, E)  Each primitive p i correspond to a vertex V(p i ) in V  Add an edge (V(p l ), V(p m )) to E if and only if ♦ p l and p m are vertex-adjacent ♦ There exists a primitive p in the mesh that both (p l, p) and (p, p m ) are adjacent

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 33 [Govindaraju 05] AABB Hierarchy Culling  Test the Hierarchy against itself  Compute non-adjacent primitive colliding 2.5D Overlap Tests  Extend CULLIDE

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 34 [Govindaraju 05] 2.5D Overlap Tests  First pass: Traverse the primitives in Si from the last to the first. Test if p i m is fully-visible against previously rendered primitives in S j, i.e. S j >m Second pass: Traverse the primitives in Si from the first to the last. Only test the primitive p i m which was fully visible in the first pass for potential overlap with the PCS S j, i.e. S j <m

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 35 [Govindaraju 05] Exact Tests: Non-Adjacent Primitives  Merge the PCS of all independent sets  Use AABB hierarchy to compute intersecting pairs  Perform EE and VF tests between pairs Exact Tests: Adjacent Primitives  Check all adjacent primitives for intersection  Do not test the shared edge or vertex

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 36 [Govindaraju 05]

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 37 [Govindaraju 05] Limitation  Mesh with fixed connectivity  Work well with a small number of overlapping pairs  Chromatic decomposition may produce a high number of independent sets

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 38 Conclusion No general or optimal method existed Approaches based on BVH have shown to be efficient Image-space techniques can achieve highly culling rate, however, is limited by discretization accuracy Self-collision still remains challenging

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 39 Some other approaches Distance Fields Spatial Subdivision Stochastic Methods (Refer to [Teschner 2005], a State of the Art review)

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL 40 References Survey: LIN M., MANOCHA D.: Collision and proximity queries. In Handbook of Discrete and Computational Geometry, 2003 Collision detection for deformable objects. Teschner, M., Kimmerle, S., Heidelberger, B., Zachmann, G., Raghupathi, L., Fuhrmann, A., Cani, M.-P., Faure, F., Magnenat-Thalmann, N., Strasser, W., and Volino, P Computer Graphics Forum Larsson T., Akenine-Möller T Collision detection for continuously deforming bodies. In Eurographics, pp. 325–333. short presentation. Interactive Collision Detection for Deformable Models Using Streaming AABBs, Xinyu Zhang, Young J. Kim, IEEE Trans Visualization & Computer Graphics, 2007 Govindaraju, N., Redon, S., Lin, M. C., and Manocha, D CULLIDE: Interactive Collision Detection between Complex Models in Large Environments using Graphics Hardware. Proc. of Eurographics/SIGGRAPH Workshop on Graphics Hardware Interactive Collision Detection between Deformable Models using Chromatic Decomposition, Naga K. Govindaraju, David Knott, Nitin Jain, Ilknur Kabul, Rasmus Tamstorf, Russel Gayle, Ming C. Lin, Dinesh Manocha in ACM SIGGRAPH 2005