A Multi-view Stereo Benchmark with High-Resolution Images and Multi-camera Videos
Abstract
Motivated by the limitations of existing multi-view stereo benchmarks, we present a novel dataset for this task. Towards this goal, we recorded a variety of indoor and outdoor scenes using a high-precision laser scanner and captured both high-resolution DSLR imagery as well as synchronized low-resolution stereo videos with varying fields-of-view. To align the images with the laser scans, we propose a robust technique which minimizes photometric errors conditioned on the geometry. In contrast to previous datasets, our benchmark provides novel challenges and covers a diverse set of viewpoints and scene types, ranging from natural scenes to man-made indoor and outdoor environments. Furthermore, we provide data at significantly higher temporal and spatial resolution. Our benchmark is the first to cover the important use case of hand-held mobile devices while also providing high-resolution DSLR camera images. We make our datasets and an online evaluation server available at http://www.eth3d.net.
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The biggest gains in performance for zero-shot transfer were achieved for datasets that feature dense, high-resolution evaluations.
“We notice that the biggest gains in performance for zero-shot transfer were achieved for datasets that feature dense, high-resolution evaluations [15,55,59].”
From Vision Transformers for Dense Prediction (2021) · cites this paper as [59]
Cites
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Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · see citing works
Structure-from-Motion Revisited
Schonberger, J. L., & Frahm, J.-M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.445 · Cited by 6,433 · see citing works
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06) · doi:10.1109/cvpr.2006.19 · Cited by 2,457 · see citing works
A Naturalistic Open Source Movie for Optical Flow Evaluation
Butler, D. J., Wulff, J., Stanley, G. B., & Black, M. J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33783-3_44 · Cited by 1,842 · see citing works
High-accuracy stereo depth maps using structured light
Scharstein, D., & Szeliski, R. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211354 · Cited by 1,542 · see citing works
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Vision Transformers for Dense Prediction
Ranftl, R., Bochkovskiy, A., & Koltun, V. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01196 · Cited by 2,373 · see citing works
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., & Zhao, H. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00987 · Cited by 1,296 · see citing works
Consistent video depth estimation
Luo, X., Huang, J.-B., Szeliski, R., Matzen, K., & Kopf, J. (2020) · ACM Transactions on Graphics · doi:10.1145/3386569.3392377 · Cited by 322 · see citing works
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow
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On failures of RGB cameras and their effects in autonomous driving applications
Secci, F., & Ceccarelli, A. (2020) · 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) · doi:10.1109/issre5003.2020.00011 · Cited by 42 · see citing works
WorldScore: A Unified Evaluation Benchmark for World Generation
Duan, H., Yu, H., Chen, S., Fei–Fei, L., & Wu, J. (2025) · 2025 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02573 · Cited by 17
“Then, we source images from open-source scene datasets [8, 38, 42, 57, 62, 67, 69, 74, 98] and supplement with an online source, Unsplash [7].”
Uncertainty Based Camera Model Selection
Polic, M., Steidl, S., Albl, C., Kukelova, Z., & Pajdla, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00603 · Cited by 5 · see citing works
- Authors
- Schops, T., Schonberger, J. L., Galliani, S., Sattler, T., Schindler, K., Pollefeys, M., & Geiger, A.
- Affiliations
- ETH Zurich (CH)
- Microsoft (United States) (US)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 2538-2547
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2741885505
- Cited by
- 915
- References
- 5
- Citations by year
- 2026: 108 · 2025: 150 · 2024: 151 · 2023: 139 · 2022: 80 · 2021: 97 · 2020: 91 · 2019: 65 · 2018: 29 · 2017: 4 · 2014: 1
- FWCI
- 15.11
- 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)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- 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 › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- multi-view stereo, benchmark dataset, high-resolution imagery, multi-camera video, mobile devices, 3D reconstruction, indoor and outdoor scenes
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