A survey of image registration techniques
ACM Computing Surveys (CSUR)
Artificial Intelligence - Special volume on computer vision
Constraint, optimization, and hierarchy: reviewing stereoscopic correspondence of complex features
Computer Vision and Image Understanding
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
ACM Computing Surveys (CSUR)
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Image Warping with Scattered Data Interpolation
IEEE Computer Graphics and Applications
Verbs and Adverbs: Multidimensional Motion Interpolation
IEEE Computer Graphics and Applications
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
A Progressive Scheme for Stereo Matching
SMILE '00 Revised Papers from Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
In defence of the 8-point algorithm
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
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Stereo vision is a long-studied problem in computer vision. Yet, few have approached it from the angle of interpolation. In this paper, we present an approach, Interpolation-based Iterative Stereo Matching (IISM), that regards stereo matching as a mapping that maps image position from one view to the corresponding position in the other view, and the mapping is to be learned or interpolated from some samples that could be just some initial correspondences over some distinct image features that are easy to match. Once the mapping is interpolated, it could be used to predict correspondences beyond the samples, and once such predicted correspondences are corrected and confirmed through local search around the predicted positions in the image data, they could be used together with the original samples as a new and larger sample for another round of interpolation. In other words, interpolation for the mapping is not one-time, but about a number of rounds of interpolation, correspondence prediction, prediction correction, sample set enlargement, and so on, each round producing a more accurate stereo correspondence mapping. IISM utilizes the Example-Based Interpolation (EBI) scheme, but in IISM the existing EBI is adapted to ensure the established correspondences satisfy exactly the epipolar constraint of the image pair, and to a certain extent preserve discontinuities in the stereo disparity space of the imaged scene. Experimental results on a number of real image datasets show that the proposed solution has promising performance even when the initial correspondence samples are sparse.