Chen Chi , Yang Bisheng

Abstract：Laser point clouds captured using terrestrial laser scanning (TLS) in an uncontrollable urban outdoor or indoor scene suffer from irregular shaped data blanks caused by dynamic occlusion that temporarily exists, i.e., moving objects, such as pedestrians or cars, resulting in integrality and quality losses of the scene data. This paper proposes a novel automatic dynamic occlusion detection and inpainting method for sequential TLS point clouds captured from one scan position. In situ collected laser point clouds sequences are indexed by establishing a novel panoramic space partition that assigns a three dimensional voxel to each laser point according to the scanning setups. Then two stationary background models are constructed at the ray voxel level using the laser reflectance intensity and geometrical attributes of the point set inside each voxel across the TLS sequence. Finally, the background models are combined to detect the points on the dynamic object, and the ray voxels of the detected dynamic points are tracked for further inpainting by replacing the ray voxels with the corresponding background voxels from another scan. The resulting scene is free of dynamic occlusions. Experiments validated the effectiveness of the proposed method for indoor and outdoor TLS point clouds captured by a commercial terrestrial scanner. The proposed method achieves high precision and recall rate for dynamic occlusion detection and produces clean inpainted point clouds for further processing.

Keywords：Terrestrial laser scanning，LiDAR data，Dynamic occlusion detection，Point clouds inpainting，Point clouds preprocessing

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