Guibin Zhang, Haiyang Yu, Kaiming Yang, Bingli Wu, Fei Huang, Yongbin Li, Shuicheng Yan. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Point cloud registration is the process of using the common structures in two point clouds to splice them together. To find out these common structures and make these structures match more accurately, interacting information of the source and target point clouds is essential. However, limited attention has been paid to explicitly model such feature interaction. To this end, we propose a Feature Interactive Representation learning Network (FIRE-Net), which can explore feature interaction among the source and target point clouds from different levels. Specifically, we first introduce a Combined Feature Encoder (CFE) based on feature interaction intra point cloud. The CFE extracts interactive features intra each point cloud and combines them to enhance the ability of the network to describe the local geometric structure. Then, we propose a feature interaction mechanism inter point clouds which includes a Local Interaction Unit (LIU) and a Global Interaction Unit (GIU). The former is used to interact information between point pairs across two point clouds, thus the point features in one point cloud and its similar point features in another point cloud can be aware of each other. The latter is applied to change the per-point features depending on the global cross information of two point clouds, thus one point cloud has the global perception of another. Extensive experiments on partially overlapping point cloud registration show that our method achieves state-of-the-art performance.

In point cloud processing, efficient feature aggregation of local regions is essential for hierarchical representations of 3D shapes. However, most prior works directly aggregate local features by symmetric functions, which will result in the desertion of vital shape information. In this paper, we propose a novel aggregation module based on 3D attention mechanism, named Local Region Attention Aggregation, which can capture the local shape implied in irregular points by emphasizing informative features and suppressing unnecessary ones. Specifically, for each local region, our module first encodes point relations, and then sequentially infers two attention maps generated by 3D geometric relations and fusion of multi-scale features respectively. After that, we multiply encoded point features with the attention maps to refine features adaptively. Consequently, by aggregating the higher-quality local features, shape awareness can be enhanced. Extensive experiments on various challenging benchmarks verify our method achieves state-of-the-art performance.