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Automatic semantic modeling of indoor scenes from low-quality RGB-D data using contextual information

Chen, Kang, Lai, Yu-Kun ORCID:, Wu, Yu-Xin, Martin, Ralph and Hu, Shi-Min ORCID: 2014. Automatic semantic modeling of indoor scenes from low-quality RGB-D data using contextual information. ACM Transactions on Graphics 33 (6) , 208. 10.1145/2661229.2661239

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We present a novel solution to automatic semantic modeling of indoor scenes from a sparse set of low-quality RGB-D images. Such data presents challenges due to noise, low resolution, occlusion and missing depth information. We exploit the knowledge in a scene database containing 100s of indoor scenes with over 10,000 manually segmented and labeled mesh models of objects. In seconds, we output a visually plausible 3D scene, adapting these models and their parts to fit the input scans. Contextual relationships learned from the database are used to constrain reconstruction, ensuring semantic compatibility between both object models and parts. Small objects and objects with incomplete depth information which are difficult to recover reliably are processed with a two-stage approach. Major objects are recognized first, providing a known scene structure. 2D contour-based model retrieval is then used to recover smaller objects. Evaluations using our own data and two public datasets show that our approach can model typical real-world indoor scenes efficiently and robustly.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA76 Computer software
Additional Information: Pdf uploaded in accordance with the publisher’s policy at (accessed 26/11/2014) © ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transaactions on Graphics {VOL 33, ISSUE 6, 2014}
Publisher: Association for Computing Machinery (ACM)
ISSN: 0730-0301
Date of First Compliant Deposit: 30 March 2016
Last Modified: 06 Nov 2023 19:47

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