@article{cai2016surface,
author = {Cai, Yiqi and Guo, Xiaohu and Liu, Yang and Wang, Wenqiang and Mao, Weihua and Zhong, Zichun},
title = {Surface Approximation via Asymptotic Optimal Geometric Partition},
year = {2016},
month = {November},
abstract = {In this paper, we present a novel method on surface partition from the perspective of approximation theory. Different from previous shape proxies, the ellipsoidal variance proxy is proposed to penalize the partition results falling into disconnected parts. On its support, the Principle Component Analysis (PCA) based energy is developed for asymptotic cluster aspect ratio and size control. We provide the theoretical explanation on how the minimization of the PCA-based energy leads to the optimal asymptotic behavior for approximation. Moreover, we show the partitions on densely sampled triangular meshes converge to the theoretic expectations. To evaluate the effectiveness of surface approximation, polygonal/triangular surface remeshing results are generated. The experimental results demonstrate the high approximation quality of our method.},
publisher = {IEEE},
url = {http://approjects.co.za/?big=en-us/research/publication/surface-approximation-via-asymptotic-optimal-geometric-partition/},
journal = {IEEE Transactions on Visualization & Computer Graphics},
edition = {IEEE Transactions on Visualization & Computer Graphics},
}