EfficientPEAL: Efficient prior-embedded attention learning for partially overlapping point cloud registration

Junle Yu, Wenhui Zhou, Zhehao Shen, Yongwei Miao

Abstract: Highlights•We propose an efficient prior-embedded learning framework for enhancing the pose registration of point clouds, demonstrating significant robustness across both high-overlap and low-overlap scenarios.•The proposed local geometric self-attention module effectively reduces the GPU memory consumption and inference time compared to previous work, while maintaining comparable performance.•The proposed method efficiently integrates various overlap priors to enhance registration performance, such as 2D prior, 3D prior, hybrid 2D/3D prior and random prior.•The proposed method outperforms the state-of-the-art on several public datasets, including 3DMatch/3DLoMatch, ScanNet and KITTI datasets, demonstrating its generality and effectiveness across different datasets.

External IDs:dblp:journals/eswa/YuZSM25