点云
激光雷达
遥感
高度(三角形)
背景(考古学)
计算机科学
基本事实
点(几何)
对象(语法)
激光扫描
计算机视觉
人工智能
地理
激光器
光学
几何学
物理
数学
考古
出处
期刊:Photogrammetric Engineering and Remote Sensing
[American Society for Photogrammetry and Remote Sensing]
日期:2024-01-01
卷期号:90 (1): 55-64
标识
DOI:10.14358/pers.23-00056r2
摘要
High altitude lidar scans allow for rapid acquisition of big spatial data representing entire city blocks. Unfortunately, the raw point clouds acquired by this method are largely incomplete due to object occlusions and restrictions in scanning angles and sensor resolution, which can negatively affect the obtained results. In recent years, many new solutions for 3D point cloud completion have been created and tested on various objects; however, the application of these methods to high-altitude lidar point clouds of buildings has not been properly investigated yet. In the above context, this paper presents the results of applying several state-of-the-art point cloud completion networks to various building exteriors acquired by simulated airborne laser scanning. Moreover, the output point clouds generated from partial data are compared with complete ground-truth point clouds. The performed tests show that the SeedFormer network trained on the ShapeNet-55 data set provides promising shape completion results.
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