用3D靶标和光线预测提升相机标定精度，解决传统误差指标误导问题。
Beyond Reprojection Error: Camera Calibration with 3D Targets
- 基于预测场景光线设计新标定框架，兼容最新相机模型。
- 3D靶标使交点误差降低约40%，稳定性显著提升。
- 适合追求高精度3D重建的科研与工业应用者。
在三维重建中，相机标定是实现高保真几何重建的关键。现有方法依赖二维平面标定，本文提出一种面向三维重建的新框架，通过预测场景光线实现更高灵活性，并支持最新相机模型。引入基于预测光线的新指标——重建误差与交点误差，结合自举法统计评估不同标定物与流程对内参、外参的性能。结果表明，广义畸变模型更真实地捕捉物理相机效应，显著提升标定精度。重投影误差可能误导3D精度判断，而所提光线基指标提供更全面评估。本文设计了二十面体标定靶标并搭配环形特征检测器，合成数据上交点误差降低约40%，自举试验更稳定；真实数据表现则依赖极高的制造公差。
原文摘要 · Abstract (English)
In 3D reconstruction, camera calibration is an essential element for achieving high fidelity and accuracy of the reconstructed geometry. While existing approaches rely upon 2D planar calibration, this work proposes a framework tailored for 3D reconstruction that is based on predicting scene rays, which adds flexibility to the reconstruction pipeline and enables the use of recent advances in camera models. Novel metrics, reconstruction and intersection error, derived from predicted scene rays are employed in combination with a bootstrapping procedure that statistically evaluates different calibration objects and calibration pipelines for both intrinsic and extrinsic camera parameters. The results show that the generalized distortion model more faithfully captures physical camera effects and yields an improvement in calibration accuracy. Reprojection error is shown to be a potentially misleading indicator of 3D accuracy, and the proposed ray-based metrics provide a more holistic assessment. An icosahedron calibration target is designed to enrich calibration information for 3D reconstruction together with a ring-feature-based detector. The icosahedral target yields approximately 40% lower mean intersection and more stable calibration across bootstrap trials on synthetic data, while real-data performance demands very tight fabrication tolerances.
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