AnyCalib: On-Manifold Learning for Model-Agnostic Single-View Camera Calibration

2025-03-16

AI Summary

This paper addresses model-free intrinsic camera calibration from a single unstructured outdoor image, without requiring gravity priors, camera-specific assumptions, or external geometric cues. We propose a pixel-ray regression-based intermediate representation that decouples intrinsic estimation into manifold-learning-driven ray modeling and model-agnostic closed-form inversion. The framework uniformly supports diverse projection models—including pinhole, Brown–Conrady, and Kannala–Brandt—and natively accommodates common image edits such as cropping and stretching. Our approach employs only a lightweight neural network, trained on orders-of-magnitude less data than 3D foundation models, yet consistently surpasses state-of-the-art methods in accuracy and robustness. Notably, it demonstrates superior cross-model generalization and resilience to edited images—key challenges in practical deployment.

Technology Category

• Computer Vision: Other Foundations of Computer Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web search models and ranking

Abstract

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.

Problem

• Calibrates camera intrinsics from single in-the-wild images
• Agnostic to camera model, uses inherent image cues
• Outperforms methods with less training data

Innovation

• Single-view calibration without camera model dependency
• Uses pixel ray regression for intrinsic parameter recovery
• Applies to edited images, outperforms 3D foundation models