Indoor scene segmentation with semantic cuboids

Indoor scene understanding is important to service robots, and scene segmentation is part of this work. This paper has proposed a method of semantic cuboids to partition a single RGB-D image. Semantic cuboids is generated by combining semantic labels and cuboid. Semantic labels are assigned by Markov random field based on super-pixels. And cuboids are constructed by graph method in a scene. Experiments on real data show that the proposed framework obtains accurate semantic cuboids in indoor environments, and completes computational tasks in real time. This method executes faster and adds more semantic information to cuboid than linear matching approach of H Jiang. The results provide useful and precise information of indoor cuboids for service robots.