Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Correspondence by Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A guided tour of computer vision
A guided tour of computer vision
Occlusions and binocular stereo
International Journal of Computer Vision
Artificial Intelligence - Special volume on computer vision
A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
A Bayesian approach to binocular stereopsis
International Journal of Computer Vision
From Multiple Stereo Views to Multiple 3-D Surfaces
International Journal of Computer Vision
Inference of Surfaces, 3D Curves, and Junctions from Sparse, Noisy, 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intensity- and Gradient-Based Stereo Matching Using Hierarchical Gaussian Basis Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inference of Integrated Surface, Curve, and Junction Descriptions From Sparse 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching with Transparency and Matting
International Journal of Computer Vision - 1998 Marr Prize
Grouping ., -, →, 0 - , into regions, curves, and junctions
Computer Vision and Image Understanding - Special issue on perceptual organization in computer vision
ACM Computing Surveys (CSUR)
Signal Processing for Computer Vision
Signal Processing for Computer Vision
Computational Framework for Segmentation and Grouping
Computational Framework for Segmentation and Grouping
Curvature-Augmented Tensor Voting for Shape Inference from Noisy 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Occlusions, Discontinuities, and Epipolar Lines in Stereo
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Photorealistic Scene Reconstruction by Voxel Coloring
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
On Occluding Contour Artifacts in Stereo Vision
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Space-Sweep Approach to True Multi-Image Matching
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Inferring Segmented Surface Description from Stereo Data
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
A Voting-Based Computational Framework for Visual Motion Analysis and Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Using Monocular Cues within the Tensor Voting Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We present an integrated approach to the derivation of scene descriptions from a pair of stereo images, where the steps of feature correspondence and surface reconstruction are addressed within the same framework. Special attention is given to the development of a methodology with general applicability. In order to handle the issues of noise, lack of image features, surface discontinuities, and regions visible in one image only, we adopt a tensor representation for the data and introduce a robust computational technique called tensor voting for information propagation. The key contributions of this paper are twofold: First, we introduce "saliency" instead of correlation scores as the criterion to determine the correctness of matches and the integration of feature matching and structure extraction. Second, our tensor representation and voting as a tool enables us to perform the complex computations associated with the formulation of the stereo problem in three dimensions at a reasonable computational cost. We illustrate the steps on an example, then provide results on both random dot stereograms and real stereo pairs, all processed with the same parameter set.