Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Learning Spatial Context: Using Stuff to Find Things
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
International Journal of Computer Vision
Learning structural SVMs with latent variables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Cutting-plane training of structural SVMs
Machine Learning
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
From 3D scene geometry to human workspace
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Teaching 3D geometry to deformable part models
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Real-time indoor scene understanding using Bayesian filtering with motion cues
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
| Hi-index | 48.22 |
We address the problem of understanding an indoor scene from a single image in terms of recovering the room geometry (floor, ceiling, and walls) and furniture layout. A major challenge of this task arises from the fact that most indoor scenes are cluttered by furniture and decorations, whose appearances vary drastically across scenes, thus can hardly be modeled (or even hand-labeled) consistently. In this paper we tackle this problem by introducing latent variables to account for clutter, so that the observed image is jointly explained by the room and clutter layout. Model parameters are learned from a training set of images that are only labeled with the layout of the room geometry. Our approach enables taking into account and inferring indoor clutter without hand-labeling of the clutter in the training set, which is often inaccurate. Yet it outperforms the state-of-the-art method of Hedau et al. that requires clutter labels. As a latent variable based method, our approach has an interesting feature that latent variables are used in direct correspondence with a concrete visual concept (clutter in the room) and thus interpretable.