1 / 24

C ONSERVATIVE V OXELIZATION

C ONSERVATIVE V OXELIZATION. Long Zhang 1 , Wei Chen 1 , David Ebert 2 , Qunsheng Peng 1 1 State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China 2 School of Electrical and Computer Engineering, Purdue University. Introduction & Previous Work. The Main Idea.

zeno
Download Presentation

C ONSERVATIVE V OXELIZATION

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. CONSERVATIVE VOXELIZATION Long Zhang1, Wei Chen1, David Ebert2, Qunsheng Peng1 1 State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China 2 School of Electrical and Computer Engineering, Purdue University

  2. Introduction & Previous Work The Main Idea Algorithm & Implementation Experimental Results Conclusions & Future Work Contents 2 / 24

  3. Introduction & Previous Work The Main Idea Algorithm & Implementation Experimental Results Conclusions & Future Work Contents 3 / 24

  4. Voxelization • Rasterization – 2D scan-conversion • Voxelization – 3D scan-conversion rasterization Geometry primitive pixels voxelization Surface model voxels 4 / 24

  5. Voxel-ization volumetric modelling haptic rendering collision detection 3D spatial analysis physically-based simulation virtual medicine Applications 5 / 24

  6. The basic idea • Use the rasterization functionality (2D scan-conversion) of graphics hardware to accelerate voxel-ization (3D scan-conversion) • Major advantage • Efficient Hardware-accelerated Voxelization 6 / 24

  7. What’s Conservative? standard rasterization conservative rasterization 7 / 24

  8. Why Is It Important? • Example: collision detection with standard rasterization conservative rasterization no collision 4 colliding pixels detected 8 / 24

  9. Introduction & Previous Work The Main Idea Algorithm & Implementation Experimental Results Conclusions & Future Work Contents 9 / 24

  10. The Main Idea • Illustration in 2D Previous approaches Conservative voxelization The viewing volume A triangle to be voxelized The resulting voxels rasterization The viewing plane • Use the depth value of the pixel center • Generate a single voxel for each pixel • Compute the depth range in each pixel • Generate multiple voxels for each pixel pixels Orthogonal projection 10 / 24

  11. Introduction & Previous Work The Main Idea Algorithm & Implementation Experimental Results Conclusions & Future Work Contents 11 / 24

  12. Computing the Depth Range Full covered pixels Partially covered pixels • Compute the pixel/triangle intersection • Compute the depth range in the intersection region 12 / 24

  13. The Pixel/Triangle Intersection • A convex polygon • The minimal/maximal depth value lies on one of its vertices • The idea • Compute the depth values at all vertices • Compare the depth values to get the depth range • Compute the intersection of two edges of the triangle and the pixel • The depth value can be easily calculated; • Is the condition satisfied? • The depth value is known; • Is the condition satisfied? (easy to know) 13 / 24

  14. The Algorithm • Compute the minimal depth for each pixel • Compute the maximal depth in the same way Let be the vertex of the pixel with the minimal depth value If is inside the triangle No Yes Text Text • None of the pixel vertices has the minimal depth • Compute the intersection points of triangle/pixel edges • Test if any triangle vertices are in the pixel = Text Text The computation is fast Compute the barycentric coordinate The computation is slow 14 / 24

  15. Robustness Issues • Handling special cases • The result is conservative Intersection of nearly parallel lines Replace QT with QR Degenerate edges Replace QR with Pj Degenerate triangles Replace v1with Q’L 15 / 24

  16. Introduction & Previous Work The Main Idea Algorithm & Implementation Experimental Results Conclusions & Future Work Contents 16 / 24

  17. ConservativeVoxelization • Theoretically • Accuracy: • A voxel is generated if and only if it intersects with the input model • In practice • Slightly over-conservative: • All voxels intersecting the input model are generated • A few voxels not intersecting the input model are also generated Accuracy Analysis 17 / 24

  18. Efficient for high resolution voxlization • Not very efficient for large model size • Analysis • – the major cost lies in computing the depth range for each pixel • – the cost is much lower for inner pixels than for boundary pixels • – most of the pixels are inner pixels when the resolution is high Timing Statistics voxelization timings in ms. Performance comparison between Dong’s and our method 18 / 24

  19. Application to Collision Detection • Basic idea • Voxelize two models with a common bounding box • Compare the resulting volumes • Evaluation • Accuracy • High resolution supported • Classfication – colliding voxels represent potentially colliding reg-ions, and noncolliding voxels rep-resent non-colliding regions • Efficiency • Computation completely in GPU • Need to traverse eachmodel once • Support deforming models Collision detection between the buddha model and a morphing hand model. The collision detection is accomplished in approximately 114 ms. 19 / 24

  20. Demo 20 / 24

  21. Introduction & Previous Work The Main Idea Algorithm & Implementation Experimental Results Conclusions & Future Work Contents 21 / 24

  22. 1 2 3 The idea We propose the idea of conservative vox-lization. • Algorithm • Fully implemented in the GPU • Conservative • Efficient • Low memory cons-umption • Suitable for def-ormable models Application We demonstrate the application of our algorithm in collision detection of complex models. Conclusions • Technical contributions 22 / 24

  23. Future Work • More efficient algorithm • Using lookup tables • Need new functionalities of graphics hardware • More applications • Solid voxelization 23 / 24

  24. Thank You ! lzhang@cad.zju.edu.cn

More Related