fCOP: Focal Length Estimation from Category-level Object Priors
This addresses a challenging problem in computer vision for applications requiring camera calibration without strong scene geometry, though it appears incremental as it builds on existing tasks.
The paper tackles monocular focal length estimation by using category-level object priors, combining depth and shape priors from images to estimate focal length in closed form, and demonstrates outperforming state-of-the-art methods on simulated and real-world data.
In the realm of computer vision, the perception and reconstruction of the 3D world through vision signals heavily rely on camera intrinsic parameters, which have long been a subject of intense research within the community. In practical applications, without a strong scene geometry prior like the Manhattan World assumption or special artificial calibration patterns, monocular focal length estimation becomes a challenging task. In this paper, we propose a method for monocular focal length estimation using category-level object priors. Based on two well-studied existing tasks: monocular depth estimation and category-level object canonical representation learning, our focal solver takes depth priors and object shape priors from images containing objects and estimates the focal length from triplets of correspondences in closed form. Our experiments on simulated and real world data demonstrate that the proposed method outperforms the current state-of-the-art, offering a promising solution to the long-standing monocular focal length estimation problem.