The Chow Form of the Essential Variety in Computer Vision
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Abstract (arXiv)
The Chow form of the essential variety in computer vision is calculated. Our derivation uses secant varieties, Ulrich sheaves and representation theory. Numerical experiments show that our formula can detect noisy point correspondences between two images.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Apr 2016.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1.
4 papers.
- 2021Galois/monodromy groups for decomposing minimal problems in 3D reconstructioncited by 12
- 2021On the Instability of Relative Pose Estimation and RANSAC's Rolecited by 10
- 2022Snapshot of Algebraic Visioncited by 9
- 2023Condition numbers in multiview geometry, instability in relative pose estimation, and RANSACcited by 1
- 2023Condition numbers in multiview geometry, instability in relative pose estimation, and RANSAC4 Oct 2023
- 2022Snapshot of Algebraic Vision20 Oct 2022
- 2021On the Instability of Relative Pose Estimation and RANSAC's Role29 Dec 2021
- 2021Galois/monodromy groups for decomposing minimal problems in 3D reconstruction10 May 2021