A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Efficient and Robust Shape Correspondence via Sparsity-Enforced Quadratic Assignment
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
In this work, we introduce a novel local pairwise descriptor and then develop a simple, effective iterative method to solve the resulting quadratic assignment through sparsity control for shape correspondence between two approximate isometric surfaces. Our pairwise descriptor is based on the stiffness and mass matrix of finite element approximation of the Laplace-Beltrami differential operator, which is local in space, sparse to represent, and extremely easy to compute while containing global
doi:10.1109/cvpr42600.2020.00953
dblp:conf/cvpr/XiangLZ20
fatcat:d6iexpserrg7vovmerwsci7ora