GLORN: Strong Generalization Fully Convolutional Network for Low-Overlap Point Cloud Registration

Existing point cloud registration models suffer from large performance loss in low-overlap scenarios, while the generalization ability of most models are weak. In this article, we design a new model for point cloud registration pursing better low-overlap performance and generalization ability. On the one hand, to solve the registration problem in low-overlap scenes, we propose a novel full convolutional network searching for super points located in the overlapping region and generating feature descriptors at the super points simultaneously. The new network aims at extracting points beyond nonoverlapping or smooth regions. On the other hand, we introduce a rotation-invariant convolution strategy for the fully convolutional model so that the extracted feature descriptors have rotation invariance, which improves the generalization performance of the features. Our method is tested on 3DMatch, 3DLoMatch, KITTI, and ETH, and compared with the state-of-the-art methods. The experimental results demonstrate that our method can achieve the best performance in low-overlap registration tasks, and it performs well across unseen scenarios with different sensor modalities.