  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.
