Structure-from-Motion Revisited
Abstract
Incremental Structure-from-Motion is a prevalent strategy for 3D reconstruction from unordered image collections. While incremental reconstruction systems have tremendously advanced in all regards, robustness, accuracy, completeness, and scalability remain the key problems towards building a truly general-purpose pipeline. We propose a new SfM technique that improves upon the state of the art to make a further step towards this ultimate goal. The full reconstruction pipeline is released to the public as an open-source implementation.
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Ambiguous environmental appearances and structural errors such as drift accumulate over time and decrease reconstruction quality.
“Moreover, ambiguous environmental appearances and structural errors such as drift accumulate over time and therefore decrease the reconstruction quality118.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [118]
Spatial reconstructions of the environment can be extracted using dedicated sensors such as LiDAR or reconstructed from multiple images via mosaicking or structure from motion/SLAM.
“Such spatial reconstructions of the environment can either be extracted by using dedicated sensors such as LiDAR116 or can be reconstructed from multiple images, either by stitching the images into a unified two-dimensional panorama (e.g., mosaicking117) or by computing the three-dimensional environment from partially overlapping images (e.g., structure from motion118 or simultaneous localization”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [118]
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- Authors
- Schonberger, J. L., & Frahm, J.-M.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 4104-4113
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2471962767
- Semantic Scholar
- e7d0c37f4f3589a3b787f39e8307704da5ed8d6c
- Cited by
- 6,433
- References
- 64
- Citations by year
- 2026: 925 · 2025: 1291 · 2024: 1169 · 2023: 917 · 2022: 625 · 2021: 575 · 2020: 429 · 2019: 263 · 2018: 149 · 2017: 76 · 2016: 14
- FWCI
- 58.07
- 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
- structure from motion, 3D reconstruction, incremental reconstruction, robustness, scalability, open source software
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