In this work, we propose a camera self-calibration algorithm for generic cameras with arbitrary non-linear distortions. We jointly learn the geometry of the scene and the accurate camera parameters without any calibration objects. Our camera model consists of a pinhole model, a fourth order radial distortion, and a generic noise model that can learn arbitrary non-linear camera distortions. While traditional self-calibration algorithms mostly rely on geometric constraints, we additionally incorporate photometric consistency. This requires learning the geometry of the scene, and we use Neural Radiance Fields (NeRF).
2021: Yoonwoo Jeong, Seokjun Ahn, C. Choy, Anima Anandkumar, Minsu Cho, Jaesik Park
https://arxiv.org/pdf/2108.13826v2.pdf
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