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
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
ACM Computing Surveys (CSUR)
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
On Occluding Contour Artifacts in Stereo Vision
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Integrated Surface, Curve and Junction Inference from Sparse 3-D Data Sets
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Extremal feature extraction from 3-D vector and noisy scalar fields
Proceedings of the conference on Visualization '98
Building 3-D Human Face Models from Two Photographs
Journal of VLSI Signal Processing Systems - Special issue on multimedia signal processing
Inference of Segmented Overlapping Surfaces from Binocular Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reconstructing Surfaces by Volumetric Regularization Using Radial Basis Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Binary-Space-Partitioned Images for Resolving Image-Based Visibility
IEEE Transactions on Visualization and Computer Graphics
Stereo Using Monocular Cues within the Tensor Voting Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
View synthesis using stereo vision
View synthesis using stereo vision
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We present an integrated approach to the derivation of scene description from binocular stereo images. By inferring the scene description directly from local measurements of both point and line correspondences, we address both the stereo correspondence problem and the surface reconstruction problem simultaneously. We introduce a robust computational technique call tensor voting for the inference of scene description in terms of surfaces, junctions, and region boundaries. The methodology is grounded in two elements: tensor calculus for representation, and non-linear voting for data communication. By efficiently and effectively collecting and analyzing neighborhood information, we are able to handle the tasks of interpolation, discontinuity detection, and outlier identification simultaneously. The proposed method is non-iterative, robust to initialization and thresholding in the preprocessing stage, and the only criticalfree parameter is the size of the neighborhood. We illustrate the approach with results on a variety of images.