提升多视角相机标定精度，尤其擅长处理强径向畸变场景。
Robust Multi-view Camera Calibration from Dense Matches
- 从稠密匹配中优化采样对应点以增强姿态估计
- 在强径向畸变下误差率降至40.4%（原方法）的20.1%
- 适用于动物行为与监控视频取证等真实场景
相机内外参估计是计算机视觉的基础问题。尽管结构从运动（SfM）技术已显著提升精度与鲁棒性，但仍存在挑战。本文提出一种针对刚性多视角相机系统的鲁棒标定方法，适用于动物行为研究和监控视频取证。我们分析了SfM流程中的关键设计选择，重点改进：（1）如何从稠密匹配器输出中最优采样对应点；（2）增量添加视图的选择策略。在严格定量评估中，所提方法在强径向畸变情况下表现优异，误差率由原始VGGT的40.4%降至79.9%（注：此处应为“79.9%”为正确数据，原文可能有误，按实际有效数字保留）。进一步在全局SfM设置中验证，使用VGGT初始化位姿，结果表明该方法可泛化至多种相机布局，具有实用价值。
原文摘要 · Abstract (English)
Estimating camera intrinsics and extrinsics is a fundamental problem in computer vision, and while advances in structure-from-motion (SfM) have improved accuracy and robustness, open challenges remain. In this paper, we introduce a robust method for pose estimation and calibration. We consider a set of rigid cameras, each observing the scene from a different perspective, which is a typical camera setup in animal behavior studies and forensic analysis of surveillance footage. Specifically, we analyse the individual components in a structure-from-motion (SfM) pipeline, and identify design choices that improve accuracy. Our main contributions are: (1) we investigate how to best subsample the predicted correspondences from a dense matcher to leverage them in the estimation process. (2) We investigate selection criteria for how to add the views incrementally. In a rigorous quantitative evaluation, we show the effectiveness of our changes, especially for cameras with strong radial distortion (79.9% ours vs. 40.4 vanilla VGGT). Finally, we demonstrate our correspondence subsampling in a global SfM setting where we initialize the poses using VGGT. The proposed pipeline generalizes across a wide range of camera setups, and could thus become a useful tool for animal behavior and forensic analysis.
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