mlsanityData to 7 Oct 2026

FAR: Flexible, Accurate and Robust 6DoF Relative Camera Pose Estimation

arXiv 2403.03221 ↗First posted 5 Mar 2024cs.CVcited by 12

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Abstract (arXiv)

Estimating relative camera poses between images has been a central problem in computer vision. Methods that find correspondences and solve for the fundamental matrix offer high precision in most cases. Conversely, methods predicting pose directly using neural networks are more robust to limited overlap and can infer absolute translation scale, but at the expense of reduced precision. We show how to combine the best of both methods; our approach yields results that are both precise and robust, while also accurately inferring translation scales. At the heart of our model lies a Transformer that (1) learns to balance between solved and learned pose estimations, and (2) provides a prior to guide a solver. A comprehensive analysis supports our design choices and demonstrates that our method adapts flexibly to various feature extractors and correspondence estimators, showing state-of-the-art performance in 6DoF pose estimation on Matterport3D, InteriorNet, StreetLearn, and Map-free Relocalization.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 58 of its 84 references that are in the map. First posted on arXiv 5 Mar 2024.

Builds on

58 of 84 references.

References · 58

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38 more
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50 most cited of 58 All 58 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 4.

12 papers.