Functional correspondence by matrix completion

Abstract

In this paper, we consider the problem of finding dense intrinsic correspondence between manifolds using the recently introduced functional framework. We pose the functional correspondence problem as matrix completion with manifold geometric structure and inducing functional localization with the $L_1$ norm. We discuss efficient numerical procedures for the solution of our problem. Our method compares favorably to the accuracy of state-of-the-art correspondence algorithms on non-rigid shape matching benchmarks, and is especially advantageous in settings when only scarce data is available.

"Functional Correspondence by Matrix Completion" (CVPR 2015): This paper, presented at one of the world's top AI conferences, is almost entirely fabricated, and its results are not reproducible

References in corpus (6)

• Manopt, a Matlab toolbox for optimization on manifolds
• Matrix Completion on Graphs
• Conformal Wasserstein distances: comparing surfaces in polynomial time
• Diffusion-geometric maximally stable component detection in deformable shapes
• Multimodal diffusion geometry by joint diagonalization of Laplacians
• Matching LBO eigenspace of non-rigid shapes via high order statistics

Cited by in corpus (4)

• Efficient Deformable Shape Correspondence via Kernel Matching
• Spectral Geometric Matrix Completion
• Efficient Globally Optimal 2D-to-3D Deformable Shape Matching
• A Combinatorial Solution to Non-Rigid 3D Shape-to-Image Matching