Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Manhattan world: orientation and outlier detection by Bayesian inference
Neural Computation
Learning associative Markov networks
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ACM SIGGRAPH 2005 Papers
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Recovering Surface Layout from an Image
International Journal of Computer Vision
3-D Depth Reconstruction from a Single Still Image
International Journal of Computer Vision
Putting Objects in Perspective
International Journal of Computer Vision
International Journal of Computer Vision
Make3D: Learning 3D Scene Structure from a Single Still Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blocks world revisited: image understanding using qualitative geometry and mechanics
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Discriminative learning with latent variables for cluttered indoor scene understanding
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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
Norm-product belief propagation: primal-dual message-passing for approximate inference
IEEE Transactions on Information Theory
From 3D scene geometry to human workspace
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Semantic structure from motion
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Bayesian geometric modeling of indoor scenes
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Recovering free space of indoor scenes from a single image
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Efficient structured prediction for 3D indoor scene understanding
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Manhattan scene understanding using monocular, stereo, and 3D features
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In this paper we propose the first exact solution to the problem of estimating the 3D room layout from a single image. This problem is typically formulated as inference in a Markov random field, where potentials count image features (e.g., geometric context, orientation maps, lines in accordance with vanishing points) in each face of the layout. We present a novel branch and bound approach which splits the label space in terms of candidate sets of 3D layouts, and efficiently bounds the potentials in these sets by restricting the contribution of each individual face. We employ integral geometry in order to evaluate these bounds in constant time, and as a consequence, we not only obtain the exact solution, but also in less time than approximate inference tools such as message-passing. We demonstrate the effectiveness of our approach in two benchmarks and show that our bounds are tight, and only a few evaluations are necessary.