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Current studies often decompose multiview registration into several individual tasks, ignoring the correlation between each stage and making certain assumptions on noise distribution without knowledge from previous stages. These issues bring difficulties in generalization to real cases. In this paper, we propose an end-to-end feature-based multiview registration model that takes a set of raw 3D point cloud fragments as input and outputs the global transformation. Unlike previous works, our method allows the exchange of information between stages. We firstly estimate pairwise registration by a attention-based model to assist feature learning. In the next stage, we utilize iteratively reweighted least squares (IRLS) algorithm to refine and obtain the global transformation. In each iteration, instead of making assumptions on noises, we directly construct a model to infer the outliers from pairwise registration so that such an inference can help synchronization produce more reliable results. To follow the process in IRLS algorithm, we propose a simple yet effective refinement module to boost feature-based pairwise estimations in an iterative manner, which can be seamlessly integrated into the IRLS procedure. Extensive experiments conducted on benchmark datasets show that the results of our proposed method outperformed existing methods.
Hu et al. (Mon,) studied this question.