A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms

Steve Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski

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.

Details

Publication typeInproceedings
Published inIEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'2006)
URLhttp://vision.middlebury.edu/mview/
Pages519-526
Volume1
AddressNew York, NY
PublisherIEEE Computer Society
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