Partial Point Cloud Registration With Deep Local Feature

Abstract

How to accurately register partial point cloud still remains a challenging task, because of its irregular and unordered structure in a non-Euclidean space, noise, outliers, and other unfavorable factors. In this paper, an effective partial point cloud registration network is proposed, by devising a two-stage deep local feature extraction process and an outlier filtering strategy. To be specific, on the one hand, to effectively capture geometric interdependency in low-level space, a local attention feature extraction module is explored to extract local contextual attention features, by highlighting different attention weights on neighborhoods. On the other hand, in local feature aggregation module, two position encoding blocks are applied to increase the receptive field of each point in high-level space. Of these, an attentive pooling can automatically learn important local features to alleviate the possible information loss. Furthermore, to derive the weight of the putative correspondence, an outlier filtering module is designed by consisting of point context normalization block, differentiable pooling and differentiable unpooling layer. Moreover, in order to enhance robustness, a weighting point cloud registration model is formulated to alleviate outliers, by considering the contribution of each correspondence. Experiments on multiple datasets demonstrate that the proposed approach is competitive to several state-of-the-art algorithms. Code is publicly available at <uri>https://github.com/zhlSunLab/DLF</uri>.