仅用一张照片即可无模型依赖地校准相机参数。
AnyCalib: On-Manifold Learning for Model-Agnostic Single-View Camera Calibration
- 通过像素射线回归实现无需特定相机模型的校准
- 在多种镜头模型上实现闭式求解，精度优于现有方法
- 适用于裁剪拉伸图像，适合真实场景应用
我们提出AnyCalib，一种从单张真实场景图像中无模型依赖地校准相机内参的方法。现有方法多针对特定相机模型或需重力方向等外参线索，而我们证明图像中的透视与畸变信息已足够支持通用校准。将校准建模为像素射线回归，首次实现对针孔、Brown-Conrady、Kannala-Brandt等多种相机模型的闭式内参恢复。该方法同样适用于裁剪和拉伸后的图像。实验表明，尽管训练数据量仅为3D基础模型的千分之一，AnyCalib仍持续领先于现有方法。代码已开源。
原文摘要 · Abstract (English)
We present AnyCalib, a method for calibrating the intrinsic parameters of a camera from a single in-the-wild image, that is agnostic to the camera model. Current methods are predominantly tailored to specific camera models and/or require extrinsic cues, such as the direction of gravity, to be visible in the image. In contrast, we argue that the perspective and distortion cues inherent in images are sufficient for model-agnostic camera calibration. To demonstrate this, we frame the calibration process as the regression of the rays corresponding to each pixel. We show, for the first time, that this intermediate representation allows for a closed-form recovery of the intrinsics for a wide range of camera models, including but not limited to: pinhole, Brown-Conrady and Kannala-Brandt. Our approach also applies to edited -- cropped and stretched -- images. Experimentally, we demonstrate that AnyCalib consistently outperforms alternative methods, including 3D foundation models, despite being trained on orders of magnitude less data. Code is available at https://github.com/javrtg/AnyCalib.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。