Perceptual Organization of 3d Surface Points

Perceptual organization is proposed as a promising intermediate process toward object recognition and reconstruction from 3D surface points, which can be derived from aerial stereo-images, LIDAR data or InSAR data. Here, perceptual organization is to group sensory primitives originating from the same object and has been emphasized as a robust intermediate-level grouping process toward object recognition in human and computer vision. Despite intensive research on 2D data, perceptual organization of 3D entities is still in its infancy, however. Therefore, the purpose of this research is to develop a robust approach for constructing perceptual organization particularly with irregularly distributed 3D surface points. The scope of perceptual organization presented in this paper is limited to signal, primitive and structural levels. At the signal level, we organize raw 3D points into spatially coherent patches. Then, at the primitive level, we merge the patches into co-parametric surfaces. Finally, at the structural level, we group the surfaces into perceptually meaningful surface clusters. We establish a novel approach and implement the approach as an autonomous system. The system is evaluated with real LIDAR data by inspecting the quality of organized output. The evaluation substantiates a promising performance of the system. The organized output serves as a valuable input to higher order perceptual processes, including the generation and validation of hypotheses in object recognition tasks.