A dynamic graph aggregation framework for 3D point cloud registration

Currently, most of the existing point cloud registration methods use stacked edge convolution as feature extractor, ignoring the importance of deep semantic information, meanwhile, the use of attention mechanism tends to increase the computational cost of the model and limits the matching effect. This paper proposes a dynamic graph aggregation framework for point cloud registration by building a dynamic deep network, which can capture richer semantic information and shape properties of point clouds. First, a hybrid feature extractor, which fully fuses local graph information and global graph information, is designed to obtain more discriminative feature descriptors. Second, a local graph neighborhood scoring module to update local graph features and achieve feature enhancement is built by learning the difference of similar point neighborhood features. Finally, a dual-constrained matching module is designed, which is used to measure the similarity of point pairs from the two aspects of Euclidean distance and affinity between features. The proposed framework achieves excellent registration performance, as demonstrated by experimental results on challenging 3D point cloud benchmarks. Especially in partial point cloud registration, MAE( R ) achieved excellent results of 0. 2614 and 0. 2619 under unseen shapes and unseen categories.

Authors

• Feilong Cao
• Jiatong Shi Carnegie Mellon University
• Chenglin Wen

Keywords

• Deep learning
• 3D point cloud
• Registration
• Dynamic graph