Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration
This addresses the problem of efficient and accurate point cloud registration for applications like robotics or 3D reconstruction, with incremental improvements in method and efficiency.
The paper tackles partially overlapping 3D point cloud registration by proposing a learning-based pipeline with iterative distance-aware similarity matrix convolution and a two-stage point elimination technique, achieving state-of-the-art performance and improved computational efficiency in experiments.
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.