利用物体类别先验，从单目图像中精确估算焦距。
fCOP: Focal Length Estimation from Category-level Object Priors
- 基于物体形状与深度先验，通过三元对应关系闭式求解焦距。
- 在真实与模拟数据上均超越当前最优方法，误差显著降低。
- 适合无场景几何先验的单目重建任务，尤其适用于移动设备应用。
在计算机视觉领域，通过视觉信号感知和重建三维世界高度依赖相机内参，该问题长期受到学界关注。在实际应用中，若缺乏如曼哈顿世界假设或特殊人工标定图案等强场景几何先验，单目焦距估计成为一项挑战性任务。本文提出一种基于类别级物体先验的单目焦距估计算法。基于已有的单目深度估计与类别级物体规范表示学习任务，我们的焦距求解器从包含物体的图像中获取深度先验与物体形状先验，通过三元对应关系闭式求解焦距。在模拟与真实世界数据上的实验表明，所提方法优于当前最先进水平，为长期存在的单目焦距估计问题提供了有前景的解决方案。
原文摘要 · Abstract (English)
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.
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