Separating objects and clutter in indoor scenes

Research output: Chapter in Book/Conference paperConference paperpeer-review

16 Citations (Scopus)


© 2015 IEEE. Objects' spatial layout estimation and clutter identification are two important tasks to understand indoor scenes. We propose to solve both of these problems in a joint framework using RGBD images of indoor scenes. In contrast to recent approaches which focus on either one of these two problems, we perform 'fine grained structure categorization' by predicting all the major objects and simultaneously labeling the cluttered regions. A conditional random field model is proposed to incorporate a rich set of local appearance, geometric features and interactions between the scene elements. We take a structural learning approach with a loss of 3D localisation to estimate the model parameters from a large annotated RGBD dataset, and a mixed integer linear programming formulation for inference. We demonstrate that our approach is able to detect cuboids and estimate cluttered regions across many different object and scene categories in the presence of occlusion, illumination and appearance variations.
Original languageEnglish
Title of host publicationIEEE Conference on Computer Vision and Pattern Recognition
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Print)9781467369640
Publication statusPublished - 2015
EventSeparating objects and clutter in indoor scenes - Boston, Massachusetts, USA
Duration: 1 Jan 2015 → …


ConferenceSeparating objects and clutter in indoor scenes
Period1/01/15 → …


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