Multi-view Registration Based on Weighted Low Rank and Sparse Matrix Decomposition of Motions

TL;DR

This paper introduces a weighted low rank and sparse matrix decomposition method for multi-view registration, improving robustness and accuracy by considering the reliability of relative motions and reducing matrix sparsity.

Contribution

It proposes a novel weighted LRS decomposition approach that accounts for motion reliability and reduces sparsity, enhancing multi-view registration performance.

Findings

Outperforms state-of-the-art methods in robustness and accuracy.

Reduces matrix sparsity to improve decomposition stability.

Demonstrates efficiency on public datasets.

Abstract

Recently, the low rank and sparse (LRS) matrix decomposition has been introduced as an effective mean to solve the multi-view registration. It views each available relative motion as a block element to reconstruct one matrix so as to approximate the low rank matrix, where global motions can be recovered for multi-view registration. However, this approach is sensitive to the sparsity of the reconstructed matrix and it treats all block elements equally in spite of their varied reliability. Therefore, this paper proposes an effective approach for multi-view registration by the weighted LRS decomposition. Based on the anti-symmetry property of relative motions, it firstly proposes a completion strategy to reduce the sparsity of the reconstructed matrix. The reduced sparsity of reconstructed matrix can improve the robustness of LRS decomposition. Then, it proposes the weighted LRS decomposition, where each block element is assigned with one estimated weight to denote its reliability. By introducing the weight, more accurate registration results can be recovered from the estimated low rank matrix with good efficiency. Experimental results tested on public data sets illustrate the superiority of the proposed approach over the state-of-the-art approaches on robustness, accuracy, and efficiency.