Computing Local Surface Orientation and Shape from Texture forCurved Surfaces
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
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
Geotensity: Combining Motion and Lighting for 3D Surface Reconstruction
International Journal of Computer Vision
Depth Estimation from Image Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Reconstruction of 3D Shapes and Scenes From A Single Image
HLK '03 Proceedings of the First IEEE International Workshop on Higher-Level Knowledge in 3D Modeling and Motion Analysis
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
ACM SIGGRAPH 2005 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
High speed obstacle avoidance using monocular vision and reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Putting Objects in Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Depth from Familiar Objects: A Hierarchical Model for 3D Scenes
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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
Example Based 3D Reconstruction from Single 2D Images
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Using depth features to retrieve monocular video shots
Proceedings of the 6th ACM international conference on Image and video retrieval
3-D Depth Reconstruction from a Single Still Image
International Journal of Computer Vision
Depth estimation using monocular and stereo cues
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Take Three Snapshots - A Tool for Fast Freehand Acquisition of 3D Objects
INTERACT '09 Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II
IEEE Transactions on Image Processing
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
Retrieving images of similar geometrical configuration
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Rendering synthetic objects into legacy photographs
Proceedings of the 2011 SIGGRAPH Asia Conference
Exploiting depth information for indoor-outdoor scene classification
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Information-gain view planning for free-form object reconstruction with a 3d ToF camera
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
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Humans have an amazing ability to perceive depth from a single still image; however, it remains a challenging problem for current computer vision systems. In this paper, we will present algorithms for estimating depth from a single still image. There are numerous monocular cues--such as texture variations and gradients, defocus, color/haze, etc.--that can be used for depth perception. Taking a supervised learning approach to this problem, in which we begin by collecting a training set of single images and their corresponding ground-truth depths, we learn the mapping from image features to the depths. We then apply these ideas to create 3-d models that are visually-pleasing as well as quantitatively accurate from individual images. We also discuss applications of our depth perception algorithm in robotic navigation, in improving the performance of stereovision, and in creating large-scale 3-d models given only a small number of images.