DAGC: Employing Dual Attention and Graph Convolution for Point Cloud based Place Recognition
Qi Sun, Hongyan Liu, Jun He, Zhaoxin Fan, Xiaoyong Du
Abstract
Point cloud based retrieval for place recognition remains to be a problem demanding prompt solution due to its difficulty in efficiently encoding local features into adequate global descriptor in scenes. Existing studies solve this problem by generating a global descriptor for each point cloud, which is used to retrieve matched point cloud in database. But existing studies do not make effective use of the relationship between points and neglect different feature's discrimination power. In this paper, we propose to employ Dual Attention and Graph Convolution for point cloud based place recognition (DAGC) to solve these issues. Specifically, we employ two modules to help extract discriminative and generalizable features to describe a point cloud. We introduce a Dual Attention module to help distinguish task-relevant features and to utilize other points' different contributions to a point to generate representation. Meanwhile, we introduce a Residual Graph Convolution Network (ResGCN) module to aggregate local features of each point's multi-level neighbor points to further improve the representation. In this way, we improve the descriptor generation by considering the importance of both point and feature and leveraging point relationship. Experiments conducted on different datasets show that our work outperforms current approaches on all evaluation metrics.