ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Manhattan world: orientation and outlier detection by Bayesian inference
Neural Computation
Colorization using optimization
ACM SIGGRAPH 2004 Papers
Geometric Context from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Boundary Extraction in Natural Images Using Ultrametric Contour Maps
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Blocks world revisited: image understanding using qualitative geometry and mechanics
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Semantic segmentation of urban scenes using dense depth maps
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Superparsing: scalable nonparametric image parsing with superpixels
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Thinking inside the box: using appearance models and context based on room geometry
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Recovering Occlusion Boundaries from an Image
International Journal of Computer Vision
From 3D scene geometry to human workspace
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
An interactive approach to semantic modeling of indoor scenes with an RGBD camera
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Patch based synthesis for single depth image super-resolution
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Object recognition robust to imperfect depth data
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
3D Wikipedia: using online text to automatically label and navigate reconstructed geometry
ACM Transactions on Graphics (TOG)
Hierarchical object discovery and dense modelling from motion cues in RGB-D video
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Contextual object category recognition for RGB-D scene labeling
Robotics and Autonomous Systems
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We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignores physical interactions or is applied only to tidy rooms and hallways. Our goal is to parse typical, often messy, indoor scenes into floor, walls, supporting surfaces, and object regions, and to recover support relationships. One of our main interests is to better understand how 3D cues can best inform a structured 3D interpretation. We also contribute a novel integer programming formulation to infer physical support relations. We offer a new dataset of 1449 RGBD images, capturing 464 diverse indoor scenes, with detailed annotations. Our experiments demonstrate our ability to infer support relations in complex scenes and verify that our 3D scene cues and inferred support lead to better object segmentation.