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Estimation-Quantization Geometry Coding using Normal Meshes

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Presentation on theme: "Estimation-Quantization Geometry Coding using Normal Meshes"— Presentation transcript:

1 Estimation-Quantization Geometry Coding using Normal Meshes
Sridhar Lavu Hyeokho Choi Richard Baraniuk Rice University

2 3D Surfaces Applications Video games Animations 3D Object modeling
e-commerce

3 3D Mesh Representation Mesh representation Problem Goal 3D scan
Point clouds Polygon mesh Problem Massive data size Michelangelo’s statue of David: > billion triangles Connectivity 0 1 2 2 3 1 0 1 4 1 2 4 2 3 4 3 0 4 Geometry Goal Compression

4 Multiscale Representation
Regular or semi-regular meshes Connectivity  Base mesh connectivity

5 Wavelet Transform Prediction residuals Wavelet transform
3D coefficients (x,y,z)

6 Normal Meshes Normal mesh representation
3D (x,y,z)  1D normal coefficient

7 Wavelet Coefficients Normal wavelet coefficients
Tangential wavelet coefficients Goal Model + Encode

8 Wavelet Coefficient Model
Statistical model for normal mesh wavelet coefficients Expectation-Quantization model [Lopresto, Orchard, Ramchandran], DCC 1997 ni ~ N(0,sigmai2) sigmai2 = local variance large  rough region small  smooth region

9 Details Causal neighborhood Quantized coefficients Estimate sigmai2
Modified model Generalized Gaussian density Fixed shape at each scale Estimate variance for each vertex

10 Vertex Scanning Order Each scale Each base triangle

11 Vertex Neighborhood

12 Estimate-Quantization Steps
Estimate step Shape parameter Variance parameter R-D optimized quantize step Rate = - log probability Distortion = MSE of coefficient Pick a lambda R-D operating point Entropy code Arithmetic coder

13 Summary

14 Error Metrics Different surfaces MSE Metro Original mesh surface
Normal re-meshing EQ algorithm coded mesh MSE Metro “average distance between two meshes” Hausdorff distance

15 PSNR Plots 0.5 – 1dB gain over zero-tree coder [Guskov, Vidimce, Sweldens, Schroder], SIGGRAPH 2000 Similar results with other data sets

16 Conclusions 0.5 – 1dB gain Over state-of-the-art mesh zerotree coder 3D surfaces much easier to compress than 2D images Very smooth (continuous) Worst case: sharp crease Future research More appropriate distortion metrics in normal mesh wavelet domain


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