  In this paper, we propose a novel learning-based pipeline for partially
overlapping 3D point cloud registration. The proposed model includes an
iterative distance-aware similarity matrix convolution module to incorporate
information from both the feature and Euclidean space into the pairwise point
matching process. These convolution layers learn to match points based on joint
information of the entire geometric features and Euclidean offset for each
point pair, overcoming the disadvantage of matching by simply taking the inner
product of feature vectors. Furthermore, a two-stage learnable point
elimination technique is presented to improve computational efficiency and
reduce false positive correspondence pairs. A novel mutual-supervision loss is
proposed to train the model without extra annotations of keypoints. The
pipeline can be easily integrated with both traditional (e.g. FPFH) and
learning-based features. Experiments on partially overlapping and noisy point
cloud registration show that our method outperforms the current
state-of-the-art, while being more computationally efficient. Code is publicly
available at https://github.com/jiahaowork/idam.
