Cascaded geometric feature modulation network for point cloud processing

Point cloud analysis is a critical technology in the field of 3D vision, such as autonomous driving and robot navigation. Utilizing the inherent geometric properties embedded in 3D point cloud data remains a great challenge. In this paper, a cascaded geometric feature modulation network is proposed to explore the shared geometric patterns of 3D point clouds from local to global. The contribution of this paper is threefold. First, we design a local geometric feature modulation (GFM) block that learns a pointwise transformation according to the surrounding semantic context of each point. Second, based on the dense connection of multiple GFM blocks, we design a novel global fusion mechanism to ensure the preservation of valuable structural information. Finally, we propose a novel spatial distribution consistency loss to remedy the situation of irrational sampling. Benefitting from the proposed loss function, our network enjoys better convergence performance at the same time. Extensive experimental results on different tasks of point cloud processing demonstrate the superiority and robustness of our proposed network.