  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 a HPMB dataset based on
the constructed LiDAR system, a High-Precision, Multi-Beam, real-world dataset.
Second, we propose an 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 3D 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 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 post-processing
of Simultaneous Localization And Mapping (SLAM), thereby improving modeling
precision in environments with sparse point clouds.
