Discriminative region learning for point cloud-based place recognition

Point cloud-based place recognition aims to estimate a rough location by searching the database with a global descriptor aggregated from local features of the query point cloud. Recent advanced methods exploit the attention mechanism that establishes all pairs of relationships to enhance the local features with long-range contextual information. However, this operation may aggregate redundant and misleading information from time-varying objects and task-irrelevant areas (such as cars and ground points) into the local features, thereby impairing the discriminative power of the features. In this paper, we propose a novel discriminative region-guided transformer, dubbed DRFormer, for the point cloud-based place recognition task by explicitly constructing discriminative pair relationships to avoid aggregating task-irrelevant information. Specifically, we devise a lightweight but effective global aggregation module, named LightVLAD, to efficiently provide cues for locating the discriminative regions. Based on the LightVLAD, we propose a discriminative region-guided attention module to pay more attention to distant discriminative local features. In this module, the approximate centers of discriminative regions are located according to the assignment weights in LightVLAD. The local regions around the centers are embedded to characterize the local contextual and structural information. Next, global interaction is performed between these embedded features, and the global information is distributed to enhance local features via cross-attention. As such, the local features attend to a small subset of discriminative regions without distraction from other irrelevant ones. Extensive experiments on various benchmark datasets demonstrate that our method outperforms existing state-of-the-art methods on the point cloud-based place recognition task.