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Haptic Rendering using Simplification

Haptic Rendering using Simplification. Comp259 Sung-Eui Yoon. Overview. Continuous-Adaptive Haptic Rendering Sensation Preserving Simplification. Haptic Rendering. 3 major steps Initializing the haptic device and transferring the dataset

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Haptic Rendering using Simplification

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  1. Haptic Rendering using Simplification Comp259 Sung-Eui Yoon

  2. Overview • Continuous-Adaptive Haptic Rendering • Sensation Preserving Simplification

  3. Haptic Rendering • 3 major steps • Initializing the haptic device and transferring the dataset • Collision detection between virtual objects and the probe • Estimating force • The force is fed to the generic probe • It require high update rates (1000Hz)

  4. Haptic Rendering Process

  5. Methods to reduce model complexity • Spatial subdivision (octree, ..) • We may lose meaningful cell that can affect user. • User can perceive incorrect force when moving fast. • Static LOD • Switching LOD may lead to noticeable changes. • There may be only one LOD for large model

  6. Continuous-Adaptive Haptic Rendering • It gives various level of detail at different regions of the surface • Also, reduce complexity of model • Doesn’t send whole geometry. • Instead, send high resolution near the probe. • Based on View-Dependence Tree for view-dependent rendering.

  7. View-Dependent Simplification • At preprocessing, calculate sequences of edge collapses by model simplification method • From this, we can make a vertex hierarchy, which is represent the way how to simplify a model at run-time

  8. View-Dependent Simplification • Switch Value • Quadric error between original geometry and simplified one. • At run-time, we calculate projection error from this. • Dependency Information • Neighboring faces when performing collapse and split to prevent foldover.

  9. View-dependent Rendering • Process active vertices list, which represent current LOD of model • Initialize active vertex node list with root nodes. • Reconstruction of a real-time adaptive mesh • Need active triangle list • There are frame-to-frame coherence

  10. Result Image

  11. Selecting LOD • Assumption • Geometry close to probe has a higher probability of collision with the probe. • So, we need more higher resolution near the probe. • How to define appropriate resolution • Bell-shaped filter, mapping table between distance and switch value.

  12. Run-time Algorithm • Scan node of vertex list • Compute the distance from the probe • Determine switch value • Compare this with the one stored in node • Split node if computed value is less than one in node and node satisfy dependency • Merge node with sibling if computed value is greater stored one of parent and the node meet dependency.

  13. Optimizations • Haptic and graphics buffers are updated in an incremental fashion • The graphics and haptic rendering require different update rate • 20Hz for graphics rendering • 1000Hz for haptic rendering • update geometry at 20 Hz

  14. Result • Use the GHOST API library. • It fails when it is pushed to run at less than 1000Hz.

  15. Limitation • Doesn’t present error metric for haptic rendering • Just use switch value for projection error. • Isn’t clear to integrate view-dependent simplification with other acceleration (Bounding Volume Hierarchy) technique for collision detection.

  16. Sensation PreservingSimplification • Key observation • Human haptic perception of geometric feature depend on the ratio between the contact area and the size of the feature • In visual rendering • Consider surface deviation and the viewing distance • In haptic rendering • Contact surface area and the resolution of the simplified model

  17. Design Issues • Design multiresolution hierarchy that : • Minimize perceptible surface deviation • Filtering the detail at appropriate resolution • Reduce the polygonal complexity of low resolution representations • Incorporating mesh decimation • Are themselves BVH of convex hull • The system take advantage of BVH of convex hull

  18. Definition of Resolution • (Sampling) Resolution r • 1D example: The inverse of the distance between two subsequent samples. • 2D : the sampling resolution of an edge (v1, v2) of the mesh M at resolution, rj ,Mj • can be estimated as the inverse of the projected length of the edge onto a low resolution representation of the mesh, Mi

  19. Filtered Edge Collapse • Two goals in the construction of hierarchy. • Generate the hierarchy with low polygonal complexity at low resolution • Filter details as we compute low resolution • These are achieved by merging downsampling and filtering operation

  20. Convexity Constraints • A surface convex decomposition for collision detection must meet these constraints • All the interior edge of a convex patch must themselves be convex. • No vertex in a convex patch may be visible from any face except the ones incident on it • The virtual face added to complete the convex hull cannot intersect the mesh

  21. Local Convexity Constraints

  22. Global Convexity Constraints • Too complicated to be incorporated into filtering process • Verified after the filtering • use bisection search between v3 and v3 if v3 meet the constraint ^ ^

  23. Multiresolution Hierarchy Generation • Starting by computing an initial convex decomposition and resolution for all the edges. • Edges are inserted in a priority queue with validity and resolution as 1st and 2nd keys for sorting. • Generating new LOD every time the number of convex pieces are halved. • Combine neighboring convex pieces as long as they represent a single valid convex patch.

  24. Contact computation for Haptics • Based on a penalty-based force computation • Force displayed is proportional to the penetration depth. • Bounding Volume Test Tree (BVTT) • Perform contact query as descending BVTT, which is dynamically constructed. • Generalized front tracking to exploit temporal coherence.

  25. BVTT and generalized front tracking

  26. Sensation Preserving Selective Refinement • Only refine the lower node of BVTT if the missing detail is perceptible. • Perceptibility • Depends on magnitude of surface feature and contact area

  27. Results

  28. Reference • M. Otaduy and M. Lin, “Sensation Preserving Simplification for Haptic Rendering”, to be appeared in SIGGRPH2003 • J. El-Sana, and A. Varshnewy, “Continuouly-Adaptive Haptic Rendering”, Virtual Environments 2000 • J.El Sana and A. Varshney. Generalized view-dependent simplification, In Proceeding EUROGRAPHICS99, pages 83-94, 1999 • M. Garland and P. Heckbert, “Surface simplification usingquadric error metrics”. In Proceedings of SIGGRAPH ’97(Los Angeles, CA), pages 209 – 216. ACM SIGGRAPH,ACM Press, August 1997. • H. Hoppe, Progressive meshes, In Proceedings SIGGRAPH 96, pages 99-108. ACM SGIGGRAPH 1996

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