  3D point cloud registration is a fundamental task in robotics and computer
vision. Recently, many learning-based point cloud registration methods based on
correspondences have emerged. However, these methods heavily rely on such
correspondences and meet great challenges with partial overlap. In this paper,
we propose ROPNet, a new deep learning model using Representative Overlapping
Points with discriminative features for registration that transforms
partial-to-partial registration into partial-to-complete registration.
Specifically, we propose a context-guided module which uses an encoder to
extract global features for predicting point overlap score. To better find
representative overlapping points, we use the extracted global features for
coarse alignment. Then, we introduce a Transformer to enrich point features and
remove non-representative points based on point overlap score and feature
matching. A similarity matrix is built in a partial-to-complete mode, and
finally, weighted SVD is adopted to estimate a transformation matrix. Extensive
experiments over ModelNet40 using noisy and partially overlapping point clouds
show that the proposed method outperforms traditional and learning-based
methods, achieving state-of-the-art performance. The code is available at
https://github.com/zhulf0804/ROPNet.
