  Unsupervised point cloud registration algorithm usually suffers from the
unsatisfied registration precision in the partially overlapping problem due to
the lack of effective inlier evaluation. In this paper, we propose a
neighborhood consensus based reliable inlier evaluation method for robust
unsupervised point cloud registration. It is expected to capture the
discriminative geometric difference between the source neighborhood and the
corresponding pseudo target neighborhood for effective inlier distinction.
Specifically, our model consists of a matching map refinement module and an
inlier evaluation module. In our matching map refinement module, we improve the
point-wise matching map estimation by integrating the matching scores of
neighbors into it. The aggregated neighborhood information potentially
facilitates the discriminative map construction so that high-quality
correspondences can be provided for generating the pseudo target point cloud.
Based on the observation that the outlier has the significant structure-wise
difference between its source neighborhood and corresponding pseudo target
neighborhood while this difference for inlier is small, the inlier evaluation
module exploits this difference to score the inlier confidence for each
estimated correspondence. In particular, we construct an effective graph
representation for capturing this geometric difference between the
neighborhoods. Finally, with the learned correspondences and the corresponding
inlier confidence, we use the weighted SVD algorithm for transformation
estimation. Under the unsupervised setting, we exploit the Huber function based
global alignment loss, the local neighborhood consensus loss, and spatial
consistency loss for model optimization. The experimental results on extensive
datasets demonstrate that our unsupervised point cloud registration method can
yield comparable performance.
