Cuboids detection in RGB-D images via Maximum Weighted Clique
Han Zhang, Xiaowu Chen, Yu Zhang, Jia Li, Qing Li, Xiaogang Wang · 2015
Cuboid detection is an essential step for understanding 3D structure of scenes. As most of indoor scene cuboids are actually objects, we propose in this paper an object-based approach to detect 3D cuboids in indoor RGB-D images. The proposed approach is learning-free and can handle general object classes rather than a limited pre-defined category set. In our approach, we first apply an extended version of the CPMC framework to generate a set of segment hypotheses, and fit a set of cuboid candidates. Given the candidate set, we select several cuboids that can provide plausible interpretations of the images by solving a Maximum Weighted Clique (MWC) problem. With this formulation, a set of ranked mid-level representations of the input image is obtained, and are further re-ranked by Maximal Marginal Relevance (MMR) measure to improve their diversity. Experimental results on NYU-V2 dataset shows that our method significantly outperforms the state-of-the-art, and shows impressive results.