%0 Conference Paper %1 1640800 %A Seitz, S.M. %A Curless, B. %A Diebel, J. %A Scharstein, D. %A Szeliski, R. %B Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on %D 2006 %K optical_flow %P 519-528 %R 10.1109/CVPR.2006.19 %T A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms %U http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1640800&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1640800 %V 1 %X This paper presents a quantitative comparison of several multi-view stereo reconstruction algorithms. Until now, the lack of suitable calibrated multi-view image datasets with known ground truth (3D shape models) has prevented such direct comparisons. In this paper, we first survey multi-view stereo algorithms and compare them qualitatively using a taxonomy that differentiates their key properties. We then describe our process for acquiring and calibrating multiview image datasets with high-accuracy ground truth and introduce our evaluation methodology. Finally, we present the results of our quantitative comparison of state-of-the-art multi-view stereo reconstruction algorithms on six benchmark datasets. The datasets, evaluation details, and instructions for submitting new models are available online at http://vision.middlebury.edu/mview.

@inproceedings{1640800, abstract = {This paper presents a quantitative comparison of several multi-view stereo reconstruction algorithms. Until now, the lack of suitable calibrated multi-view image datasets with known ground truth (3D shape models) has prevented such direct comparisons. In this paper, we first survey multi-view stereo algorithms and compare them qualitatively using a taxonomy that differentiates their key properties. We then describe our process for acquiring and calibrating multiview image datasets with high-accuracy ground truth and introduce our evaluation methodology. Finally, we present the results of our quantitative comparison of state-of-the-art multi-view stereo reconstruction algorithms on six benchmark datasets. The datasets, evaluation details, and instructions for submitting new models are available online at http://vision.middlebury.edu/mview.}, added-at = {2013-05-20T22:14:05.000+0200}, author = {Seitz, S.M. and Curless, B. and Diebel, J. and Scharstein, D. and Szeliski, R.}, biburl = {https://www.bibsonomy.org/bibtex/2d3c293395e28dd12f2fb9e3c4d4d5ef8/alex_ruff}, booktitle = {Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on}, description = {IEEE Xplore - A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms}, doi = {10.1109/CVPR.2006.19}, interhash = {b633c7562326fd1b3471fd9e25f6566c}, intrahash = {d3c293395e28dd12f2fb9e3c4d4d5ef8}, issn = {1063-6919}, keywords = {optical_flow}, pages = {519-528}, timestamp = {2013-05-20T22:14:06.000+0200}, title = {A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms}, url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1640800&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1640800}, volume = 1, year = 2006 }