Résumé
Object Segmentation is an important step in object reconstruction from point cloud data of complex urban scenes and in applications to virtual environment. This paper focuses on strategies to extract objects in 3D urban scenes for further object recognition and object reconstruction. Segmentation strategies are proposed according to object shape features. Rough segmentation is first adopted for objects classification, and further detailed segmentation is implemented for object components. Normal directions are adopted to segment each planar region, so that architectures and the ground can be extracted from other objects. Architectural components are further extracted through an analysis of planar residuals, and the residuals are used to choose seed points for region growing. And meanwhile, the size of segmental regions is used to determine whether or not it includes sparse noisy points. Experimental results on the scene scan data demonstrate that the proposed approach is effective in object segmentation, so that more details and more concise models can be obtained corresponding to real outdoor objects.