发表机构
CHU Clermont-Ferrand; SURGAR, Surgical Augmented Reality; School of Engineering, Westlake University; School of Design, Hunan University（克莱蒙费朗大学中心医院; 外科增强现实研究组; 西湖大学工程学院; 湖南大学设计学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出了首个无需校准靶标的卷帘快门相机自校准方法，通过结合两种互补成像模型构建统一双投影模型，经仿真与真实数据实验验证了方法的准确性与有效性。
AI 中文摘要
卷帘快门（Rolling Shutter, RS）相机广泛应用于消费类设备，但其逐行曝光特性会在运动状态下产生畸变，使得三维几何视觉问题的求解同时依赖相机内参和读出时间比。现有RS校准方法依赖校准靶标或专用硬件，限制了其在非约束场景下的应用。本文提出了首个RS相机自校准方法，无需校准靶标即可直接从图像序列中估计相机内参与读出时间比。该方法以自校准光束平差法（Bundle Adjustment, BA）实现，核心依赖RS成像模型，结合两种互补模型：第一种将RS成像建模为逐行位姿表示下的连续时间轨迹估计；第二种将RS图像视为时间扭曲的全局快门（Global Shutter, GS）图像，需估计校正场。两种模型的结合并非简单叠加，最终形成统一的双投影模型，其中每个三维点会在逐行相关时间戳和参考时间戳处受共享连续轨迹约束，从而强化几何与时间一致性。大量仿真分析了不同条件下多种实现方式的适用性，真实数据实验验证了所提方法的准确性、鲁棒性与实际有效性。
英文摘要
Rolling shutter (RS) cameras are widely used in consumer devices, but their row-wise exposure causes distortions under motion, making geometric 3D vision problems dependent on both camera intrinsics and readout time ratio. Existing RS calibration methods rely on calibration targets or specialised hardware, limiting their use in unconstrained settings. We present the first self-calibration method for RS cameras that directly estimates camera intrinsics and the readout time ratio from image sequences, without requiring calibration targets. The method is implemented as a self-calibrating bundle adjustment (BA), which critically depends on the RS imaging model. We combine two known complementary models. The first formulates RS imaging as continuous-time trajectory estimation under a row-wise pose representation. The second interprets RS images as temporally distorted global shutter (GS) images and requires to estimate correction fields. The combination is non-trivial and results in a unified dual-projection model, in which each 3D point is simultaneously constrained at both row-dependent and reference timestamps along a shared continuous trajectory, enforcing stronger geometric and temporal consistency. Extensive simulations analyse the applicability of several implementations under varying conditions, and real data experiments demonstrate the accuracy, robustness, and practical effectiveness of the proposed approach.
