PointGS: Bridging and fusing geometric and semantic space for 3D point cloud analysis

Directly processing 3D point cloud data becomes dominant in classification and segmentation tasks. Present mainstream point based methods usually focus on learning in either geometric space (i.e. PointNet++) or semantic space (i.e. DGCNN). Owing to the irregular and unordered data property of point cloud, these methods still suffer from drawbacks of either ambiguous local features aggregation in geometric space or poor global features extraction in semantic space. While few prior works address these two defects simultaneously by fusing information from the dual spaces, we make a first attempt to develop a synergistic framework, called PointGS. Leveraging both the strength of geometric structure and semantic representation, PointGS establishes a mutual supervision mechanism that can bridge the two spaces and fuse complementary information for better analyzing 3D point cloud data. Compared with existing popular networks, our work attains obvious performance improvement on all three mainstream tasks without any sophisticated operations.

Automated summary

This research develops a synergistic framework called PointGS, which leverages the strengths of geometric structure and semantic representation to establish a mutual supervision mechanism for analyzing 3D point cloud data. Compared to existing methods, PointGS achieves noticeable performance improvements in mainstream tasks.