AI 中文总结
本研究提出一种三轴反向投影补全方法，结合FAST-LIVO2与MLS滤波，填补高速列车车头玻璃窗区域的稀疏点云孔洞，并通过贪心投影三角化等生成网格，实现非接触式数字建模。
AI 中文摘要
在具有玻璃窗的环境中，LiDAR扫描中的孔洞问题在三维重建中仍未得到解决。本研究提出了一种点云采集、滤波、补全和表面重建的流程，以解决高速列车车头扫描中的稀疏采样和缺失窗户区域问题。FAST-LIVO2通过多传感器里程计和建图提供初始点云，移动最小二乘法（MLS）对观测数据进行平滑处理。随后，我们引入了基于三轴投影的细分和插值方法，并结合反向孔洞边界识别，称为三轴反向补全。该方法从每个孔洞周围的观测数据中插值缺失区域。贪心投影三角化、泊松表面重建以及基于Marching Cubes的流程从补全后的点云生成网格。在一个按比例缩放的展示模型上的实验表明，所提出的方法填补了玻璃窗周围的缺失点云区域。在本研究使用的评估设置下，贪心投影三角化产生的几何距离误差低于其他两种重建流程。该流程支持列车车头几何的非接触式数字建模，并为重建具有玻璃窗的物体提供了一种实用方法。
英文摘要
Holes in LiDAR scans of environments with glass windows remain an unresolved problem in three-dimensional reconstruction. This study presents a pipeline for point cloud acquisition, filtering, completion, and surface reconstruction to address sparse sampling and missing window regions in scans of a high-speed train nose. FAST-LIVO2 provides the initial point cloud through multisensor odometry and mapping, and moving least squares (MLS) smooths the observations. We then introduce three-axis projection-based subdivision and interpolation with reverse hole boundary identification, referred to as three-axis reverse completion. The method interpolates missing regions from observations around each hole. Greedy projection triangulation, Poisson surface reconstruction, and a Marching Cubes-based pipeline generate meshes from the completed point cloud. Experiments on a proportionally scaled display model of a high-speed train nose show that the proposed method fills missing point cloud regions around the glass windows. Under the evaluation setting used in this study, greedy projection triangulation yields lower geometric distance errors than the other two reconstruction pipelines. The pipeline supports non-contact digital modeling of train nose geometry and provides a practical approach to reconstructing objects with glass windows.
Comments16 pages, 6 figures
