DSMNet: Deep High-Precision 3-D Surface Modeling From Sparse Point Cloud Frames

Existing point cloud modeling datasets primarily express the modeling precision by pose or trajectory precision rather than the point cloud modeling effect itself. Under this demand, we first independently construct a set of LiDAR system with an optical stage, and then we build an HPMB dataset based on the constructed LiDAR system, a High-Precision, Multi-Beam, real-world dataset. Second, we propose a modeling evaluation method based on HPMB for object-level modeling to overcome this limitation. In addition, the existing point cloud modeling methods tend to generate continuous skeletons of the global environment, hence lacking attention to the shape of complex objects. To tackle this challenge, we propose a novel learning-based joint framework, DSMNet, for high-precision 3-D surface modeling from sparse point cloud frames. DSMNet comprises density-aware point cloud registration (PCR) and geometry-aware point cloud sampling (PCS) to effectively learn the implicit structure feature of sparse point clouds. Extensive experiments demonstrate that DSMNet outperforms the state-of-the-art methods in PCS and PCR on the Multi-View Partial Point Cloud (MVP) database. Furthermore, the experiments on the open-source KITTI and our proposed HPMB datasets show that DSMNet can be generalized as a postprocessing of simultaneous localization and mapping (SLAM), thereby improving modeling precision in environments with sparse point clouds.