Naga K. Govindaraju, Stephane Redon, Ming C. Lin, Dinesh Manocha

1. CULLIDE: Interactive Collision Detection Between Complex Models in Large Environments using Graphics Hardware Naga K. Govindaraju, Stephane Redon, Ming C. Lin, Dinesh Manocha University of North Carolina at Chapel Hill

2. Problem • Given a large environment with moving objects, • Compute overlapping objects • Overlapping triangles in each pair

3. Goal Interactive collision detectionbetween complex objects • Large number of objects • High primitive count • Non-convex objects • Open and closed objects

4. Non-rigid Motion • Deformable objects • Changing topology

5. Real-time Collision Detection using our Approach Complex breaking objects on a NVIDIA GeForce FX5800 Ultra GPU

6. Related Work • Collision detection • Hardware accelerated techniques

7. Related Work • Object space techniques • Two phases • Broad phase – Compute object pairs in close proximity • Narrow phase – Check each pair for exact collision detection

8. Related Work • Broad phase • Spatial partitioning • Sweep-and-prune • Narrow phase • Convex objects • Spatial partitioning • Bounding volume hierarchies Surveys in [Klosowski 1998, Lin and Manocha 2003, Redon et al. 2002]

9. Limitations of Object-space Techniques • Considerable pre-processing • Hard to achieve real-time performance on complex deformable models

10. Collision Detection using Graphics Hardware • Primitive rasterization – sorting in screen-space • Interference tests

11. Image Space Techniques Use of graphics hardware • CSG rendering [Goldfeather et al. 1989, Rossignac et al. 1990] • Interferences and cross-sections [Baciu et al. 1998, Myszkowski 1995, Rossignac et al. 1992, Shinya and Forgue 1991] • Minkowski sums [Kim et al. 2001] • Cloth animation [Vassilev et al. 2001] • Virtual Surgery [Lombardo et al. 1999] • Proximity computation [Hoff et al. 2001, 2002]

12. Limitations of Current Approaches • Closed models • Frame buffer readbacks – slow

13. CPU, GPU and Bandwidth Growth on Consumer PCs

14. CPU Growth Rate Courtesy: Anselmo Lastra

15. GPU Growth Rate CPU Growth Rate Courtesy: Anselmo Lastra

16. GPU Growth Rate CPU Growth Rate AGP Bandwidth Growth Rate Courtesy: Anselmo Lastra

17. Readback Timings • NVIDIA GeForce 4 • Dell Precision Workstation • 1Kx1K depth buffer – 50ms

18. Outline • Overview • Pruning Algorithm • Implementation and Results • Conclusions and Future Work

19. Outline • Overview • Pruning Algorithm • Implementation and Results • Conclusions and Future Work

20. Overview • Potentially Colliding Set (PCS) computation • Exact collision tests on the PCS

21. GPU based PCS computation Using CPU Algorithm Object LevelPruning Sub-objectLevelPruning Exact Tests

22. PCS Potentially Colliding Set (PCS)

23. Potentially Colliding Set (PCS) PCS

24. Outline • Problem Overview • Overview • Pruning Algorithm • Implementation and Results • Conclusions and Future Work

25. Algorithm Object LevelPruning Sub-object LevelPruning Exact Tests

26. Visibility Computations Lemma 1: An object O does not collide with a set of objects S if O is fully visible with respect to S • Utilize visibility for PCS computation

27. set S Object O Fully Visible Collision Detection using Visibility Computations

28. PCS Pruning Lemma 2: Given n objectsO1,O2,…,On , an object Oi does notbelong to PCS if it does notcollide with O1,…,Oi-1,Oi+1,…,On • Prune objects that do not collide

29. PCS Pruning O1 O2 … Oi-1 Oi Oi+1 … On-1 On O1 O2 … Oi-1OiOi+1 … On-1 On O1 O2 … Oi-1 Oi Oi+1 … On-1 On

30. PCS Pruning O1 O2 … Oi-1 Oi

31. PCS Pruning Oi Oi+1 … On-1 On

32. PCS Computation • Each object tested against all objects but itself • Naive algorithm is O(n2) • Linear time algorithm • Uses two pass rendering approach • Conservative solution

33. Render PCS Computation: First Pass O1 O2 … Oi-1 Oi Oi+1 … On-1 On

34. Render Fully Visible? PCS Computation: First Pass O1

35. Render Yes. Does not collide withO1,O2,…,Oi-1 Fully Visible? PCS Computation: First Pass O1 O2 … Oi-1 Oi

36. Render Fully Visible? PCS Computation: First Pass O1 O2 … Oi-1 Oi Oi+1 … On-1 On

37. Render PCS Computation: Second Pass O1 O2 … Oi-1 Oi Oi+1 … On-1 On On

38. Render Fully Visible? PCS Computation: Second Pass On

39. Render Yes. Does not collide with Oi+1,…,On-1,On Fully Visible? PCS Computation: Second Pass Oi Oi+1 … On-1 On

40. Render Yes. Does not collide with O1,…,On-1,On PCS Computation: Second Pass O1 O2 … Oi-1 Oi Oi+1 … On-1 On Fully Visible?

41. Fully Visible Fully Visible PCS Computation O1 O2 … Oi-1 Oi Oi+1 … On-1 On

42. O1 O2 O3 … Oi-1 Oi Oi+1 … On-2 On-1 On PCS Computation O1 O3 … Oi-1 Oi+1 … On-1

43. Example O1 O2 O3 O4 Scene with 4 objectsO1and O2 collideO3, O4 do not collide Initial PCS = { O1,O2,O3,O4 }

44. First Pass Fully Visible Fully Visible Not Fully Visible O1 O3 Fully Visible O4 O1 O2 O3 O4 Order of rendering: O1 O4

45. Fully Visible Fully Visible Not Fully Visible O2 O3 Fully Visible O4 Second Pass O1 O2 O3 O4 Order of rendering: O4 O1

46. Fully Visible Fully Visible After two passes O1 O2 O3 O4

47. Potential Colliding Set O1 O2 PCS ={O1,O2}

48. Algorithm Object LevelPruning Sub-object LevelPruning Exact Tests

49. Overlap Localization • Each object is composed of sub-objects • We are given n objects O1,…,On • Compute sub-objects of an object Oi that overlap with sub-objects of other objects

50. Overlap Localization • Our solution • Test if each sub-object of Oi overlaps with sub-objects of O1,..Oi-1 • Test if each sub-object of Oi overlaps with sub-objects of Oi+1,...,On • Linear time algorithm • Extend the two pass approach