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.

翻译：现有点云建模数据集主要通过位姿或轨迹精度而非点云建模效果本身来表达建模精度。在此需求下，我们首先自主搭建了一套集成光学平台的激光雷达系统，进而基于该系统构建了高精度多波束真实世界数据集HPMB。其次，我们提出一种基于HPMB的物体级建模评估方法以克服上述局限性。此外，现有点云建模方法倾向于生成全局环境的连续骨架，因而缺乏对复杂物体形状的关注。为解决这一挑战，我们提出一种新型学习型联合框架DSMNet，用于从稀疏点云帧实现高精度三维表面建模。DSMNet包含密度感知点云配准（PCR）与几何感知点云采样（PCS），可有效学习稀疏点云的隐式结构特征。大量实验表明，在Multi-View Partial Point Cloud (MVP)数据库上，DSMNet在点云采样与配准任务中均优于现有最先进方法。此外，在开源KITTI及我们提出的HPMB数据集上的实验证明，DSMNet可作为同步定位与地图构建（SLAM）的后处理模块进行泛化，从而提升稀疏点云环境中的建模精度。