Taylor Prediction for Mesh Geometry Compression

摘要: In this paper, we introduce a new formalism for mesh geometry prediction. We derive class of smooth linear predictors from simple approach based on the Taylor expansion function. use method as generic way to compute weights various used compression and compare them with those existing methods. show that our scheme is actually equivalent Modified Butterfly subdivision wavelet compression. also build efficient can be connectivity-driven in place other schemes like Average/Dual Parallelogram Prediction High Degree Polygon Prediction. The same neighbourhood, but do not make any assumption anisotropy. case Average Prediction, improve rates 3% 18% test meshes. For Dual are previous Freelence approach, outperforms traditional by 16% average. Our effectively shows these optimal Modifying free because only prediction have modified code.