IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Performance Analysis of Stereo, Vergence, and Focus as Depth Cues for Active Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum-likelihood depth-from-defocus for active vision
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 3 - Volume 3
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Geometric Context from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
High speed obstacle avoidance using monocular vision and reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
3-D Depth Reconstruction from a Single Still Image
International Journal of Computer Vision
Robotic Grasping of Novel Objects using Vision
International Journal of Robotics Research
Stereo effect of image converted from planar
Information Sciences: an International Journal
Inter-Image Statistics for 3D Environment Modeling
International Journal of Computer Vision
Belief Propagation for Depth Cue Fusion in Minimally Invasive Surgery
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Make3D: depth perception from a single still image
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Monocular vision SLAM for indoor aerial vehicles
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A close-form iterative algorithm for depth inferring from a single image
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
3D information extraction using Region-based Deformable Net for monocular robot navigation
Journal of Visual Communication and Image Representation
Continuous markov random fields for robust stereo estimation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Combining monocular geometric cues with traditional stereo cues for consumer camera stereo
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Depth recovery from a single defocused image based on depth locally consistency
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Artificial Intelligence in Medicine
A robust cost function for stereo matching of road scenes
Pattern Recognition Letters
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Depth estimation in computer vision and robotics is most commonly done via stereo vision (stereopsis), in which images from two cameras are used to triangulate and estimate distances. However, there are also numerous monocular visual cues--such as texture variations and gradients, defocus, color/haze, etc. --that have heretofore been little exploited in such systems. Some of these cues apply even in regions without texture, where stereo would work poorly. In this paper, we apply a Markov Random Field (MRF) learning algorithm to capture some of these monocular cues, and incorporate them into a stereo system. We show that by adding monocular cues to stereo (triangulation) ones, we obtain significantly more accurate depth estimates than is possible using either monocular or stereo cues alone. This holds true for a large variety of environments, including both indoor environments and unstructured outdoor environments containing trees/forests, buildings, etc. Our approach is general, and applies to incorporating monocular cues together with any off-the-shelf stereo system.