A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
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.
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The presence of nonrigid or independent motion makes collecting a ground-truth dataset for optical flow far harder than for stereo.
“The presence of nonrigid or independent motion makes collecting a ground-truth dataset for optical flow far harder than for stereo, say, where structured light (Scharstein and Szeliski 2002) or range scanning (Seitz et al. 2006) can be used to obtain ground truth.”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Seitz et al. 2006
We also compute robust accuracy measures similar to those in Seitz et al. (2006).
“We also compute robust accuracy measures similar to those in Seitz et al. (2006): A X denotes the accuracy of the error measure at the Xth percentile, after sorting the errors from low to high.”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Seitz et al. (2006)
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- Authors
- Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R.
- Affiliations
- University of Washington (US)
- Stanford University (US)
- Middlebury College (US)
- Microsoft Research (United Kingdom) (GB)
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 1
- Pages
- 519-528
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2160014001
- Cited by
- 2,457
- References
- 70
- Citations by year
- 2026: 60 · 2025: 94 · 2024: 118 · 2023: 131 · 2022: 94 · 2021: 142 · 2020: 134 · 2019: 129 · 2018: 116 · 2017: 150 · 2016: 146 · 2015: 149 · 2014: 177 · 2013: 151 · 2012: 157
- FWCI
- 70.73
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- multi-view stereo, 3D reconstruction, algorithm comparison, camera calibration, benchmark dataset, quantitative evaluation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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