A stochastic approach to stereo vision
Readings in computer vision: issues, problems, principles, and paradigms
A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory of Shape by Space Carving
International Journal of Computer Vision - Special issue on Genomic Signal Processing
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Multi-camera Scene Reconstruction via Graph Cuts
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Photorealistic Scene Reconstruction by Voxel Coloring
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Fast Stereo Matching Using Reliability-Based Dynamic Programming and Consistency Constraints
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fast Unambiguous Stereo Matching Using Reliability-Based Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geo-Consistency for Wide Multi-Camera Stereo
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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A regularization-based approach to 3D reconstruction from multiple images is proposed. As one of the most widely used multiple-view 3D reconstruction algorithms, Space Carving can produce a Photo Hull of a scene, which is at best a coarse volumetric model. The two-view stereo algorithm, on the other hand, can generate a more accurate reconstruction of the surfaces, provided that a given surface is visible to both views. The proposed method is essentially a data fusion approach to 3D reconstruction, combining the above two algorithms by means of regularization. The process is divided into two steps: (1) computing the Photo Hull from multiple calibrated images and (2) selecting two of the images as input and solving the two-view stereo problem by global optimization, using the Photo Hull as the regularizer. Our dynamic programming implementation of this regularization-based stereo approach potentially provides an efficient and robust way of reconstructing 3D surfaces. The results of an implementation of this theory is presented on real data sets and compared with peer algorithms.