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Haptic Rendering using Simplification Comp259 Sung-Eui Yoon.

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Presentation on theme: "Haptic Rendering using Simplification Comp259 Sung-Eui Yoon."— Presentation transcript:

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 Preserving Simplification 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 1 st and 2 nd 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 using quadric 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, pa ges 99-108. ACM SGIGGRAPH 1996


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