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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Han Zhang Xiaowu Chen Yu Zhang Jia Li Qing Li Xiaogang Wang |
| Copyright Year | 2015 |
| Description | Author affiliation: State Key Lab. of Virtual Reality Technol. & Syst., Beihang Univ., Beijing, China (Han Zhang; Xiaowu Chen; Yu Zhang; Jia Li; Xiaogang Wang) || Beijing Key Lab. of Inf. Service Eng., Beijing Union Univ., Beijing, China (Qing Li) |
| Abstract | 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. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 1055064 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479970827 |
| DOI | 10.1109/ICME.2015.7177405 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-06-29 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Three-dimensional displays Image segmentation Detectors Reliability Training Layout Cameras maximum weighted clique Cuboid detection scene understanding depth image |
| Content Type | Text |
| Resource Type | Article |
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