Graph based over-segmentation of 3D point cloud representation of urban scenes

In the field of computer vision, over-segmentation, or super-pixels generation, has become a popular preliminary stage for high level image analysis processes (such as classification, registration, detection and recognition). New acquisition technologies, like RGBD or LiDAR cameras, enable the capturing of 3D point clouds containing both color and geometrical information. We propose a new generic approach for over-segmentation of 3D point clouds named Point Cloud Local Variation (PCLV).In the proposed approach, points are unified, following a similarity criteria of color and geometry, to form clusters named super-points, the 3D extension of super-pixels. The feasibility of the proposed approach is demonstrated on scanned outdoor urban scenes.