Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
SMBV '01 Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV'01)
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2005 Papers
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Journal of Mathematical Imaging and Vision
Putting Objects in Perspective
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Make3D: Learning 3D Scene Structure from a Single Still Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth estimation using monocular and stereo cues
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-view Superpixel Stereo in Urban Environments
International Journal of Computer Vision
Accurate, Dense, and Robust Multiview Stereopsis
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
Active learning for piecewise planar 3D reconstruction
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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This paper presents an algorithm for considering both stereo cues and structural priors to obtain a geometrically representative depth map from a narrow baseline stereo pair. We use stereo pairs captured with a consumer stereo camera and observe that traditional depth estimation using stereo matching techniques encounters difficulties related to the narrow baseline relative to the depth of the scene. However, monocular geometric cues based on attributes such as lines and the horizon provide additional hints about the global structure that stereo matching misses. We merge both monocular and stereo matching features in a piecewise planar reconstruction framework that is initialized with a discrete inference step, and refined with a continuous optimization to encourage the intersections of hypothesized planes to coincide with observed image lines. We show through our results on stereo pairs of manmade structures captured outside of the lab that our algorithm exploits the advantages of both approaches to infer a better depth map of the scene.