Higher-order class-specific priors for semantic segmentation of 3D outdoor scenes

Given 3D outdoor scenes acquired by a LIDAR sensor, we address the problem of semantic segmentation of 3D point clouds involving simultaneously segmenting and classifying the data. The capability of semantic segmentation is essential for several applications, such as autonomous robot navigation and 3D reconstruction of point clouds. In this paper, we present a higher-order class-specific CRF model according to the discriminative priors of object classes to capture the rich statistics of natural scenes. Consequently we cast this model in an energy minimization framework and propose the detailed energy potentials based on the class-specific priors of higher-order cliques. Then the SOSPD algorithm is adapted to optimize the energy function. To evaluate the performance of our method, we provide both quantitative and qualitative results on a challenging dataset. The results show an average Fl-score of 0.82 compared to the state-of-the-art Fl-score of 0.73.