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Recently, the low rank and sparse (LRS) matrix decomposition has beenintroduced as an effective mean to solve the multi-view registration. It viewseach available relative motion as a block element to reconstruct one matrix soas to approximate the low rank matrix, where global motions can be recoveredfor multi-view registration. However, this approach is sensitive to thesparsity of the reconstructed matrix and it treats all block elements equallyin spite of their varied reliability. Therefore, this paper proposes aneffective approach for multi-view registration by the weighted LRSdecomposition. Based on the anti-symmetry property of relative motions, itfirstly proposes a completion strategy to reduce the sparsity of thereconstructed matrix. The reduced sparsity of reconstructed matrix can improvethe robustness of LRS decomposition. Then, it proposes the weighted LRSdecomposition, where each block element is assigned with one estimated weightto denote its reliability. By introducing the weight, more accurateregistration results can be recovered from the estimated low rank matrix withgood efficiency. Experimental results tested on public data sets illustrate thesuperiority of the proposed approach over the state-of-the-art approaches onrobustness, accuracy, and efficiency.

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