Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Introduction to algorithms
Simplicial pivoting for mesh generation of implicitly defined surfaces
Computer Aided Geometric Design
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Complex-valued contour meshing
Proceedings of the 7th conference on Visualization '96
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
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
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
Robust epipolar geometry estimation using genetic algorithm
Pattern Recognition Letters
Inference of Integrated Surface, Curve, and Junction Descriptions From Sparse 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Framework for Segmentation and Grouping
Computational Framework for Segmentation and Grouping
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Tensor voting for salient feature inference in computer vision
Tensor voting for salient feature inference in computer vision
Tensor voting in computer vision, visualization, and higher dimensional inferences
Tensor voting in computer vision, visualization, and higher dimensional inferences
Recovering Epipolar Geometry by Reactive Tabu Search
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
The asymptotic decider: resolving the ambiguity in marching cubes
VIS '91 Proceedings of the 2nd conference on Visualization '91
Binary-Space-Partitioned Images for Resolving Image-Based Visibility
IEEE Transactions on Visualization and Computer Graphics
International Journal of Computer Vision
First Order Augmentation to Tensor Voting for Boundary Inference and Multiscale Analysis in 3D
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Two-View Epipolar Geometry Estimation and Motion Segmentation by 4D Tensor Voting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Estimation of Adaptive Tensors of Curvature by Tensor Voting
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterated tensor voting and curvature improvement
Signal Processing
Computer Vision and Image Understanding
Computer Vision and Image Understanding
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
The Journal of Machine Learning Research
Inference of multiple subspaces from high-dimensional data and application to multibody grouping
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Robust fault matched optical flow detection using 2d histogram
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
Edge-preserving color image denoising through tensor voting
Computer Vision and Image Understanding
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We address the problem of epipolar geometry estimation efficiently and effectively, by formulating it as one of hyperplane inference from a sparse and noisy point set in an 8D space. Given a set of noisy point correspondences in two images of a static scene without correspondences, even in the presence of moving objects, our method extracts good matches and rejects outliers. The methodology is novel and unconventional, since, unlike most other methods optimizing certain scalar, objective functions, our approach does not involve initialization or any iterative search in the parameter space. Therefore, it is free of the problem of local optima or poor convergence. Further, since no search is involved, it is unnecessary to impose simplifying assumption (such as affine camera or local planar homography) to the scene being analyzed for reducing the search complexity. Subject to the general epipolar constraint only, we detect wrong matches by a novel computation scheme, 8D Tensor Voting, which is an instance of the more general N-dimensional Tensor Voting framework. In essence, the input set of matches is first transformed into a sparse 8D point set. Dense, 8D tensor kernels are then used to vote for the most salient hyperplane that captures all inliers inherent in the input. With this filtered set of matches, the normalized Eight-Point Algorithm can be used to estimate the fundamental matrix accurately. By making use of efficient data structure and locality, our method is both time and space efficient despite the higher dimensionality. We demonstrate the general usefulness of our method using example image pairs for aerial image analysis, with widely different views, and from nonstatic 3D scenes (e.g., basketball game in an indoor stadium). Each example contains a considerable number of wrong matches.