利用人体和棍状物姿态，无工具实现体育运动多摄像头自动标定。
Multi-Camera Self-Calibration in Sports Motion Capture: Leveraging Human and Stick Poses
- 通过人体关键点与已知长度的棍状物联合约束优化相机外参。
- 在4类运动、3~10台摄像机上实现低旋转与平移误差的精准标定。
- 适合无需专用标定设备的体育动作捕捉场景，如高尔夫、冰球等。
多摄像头系统广泛用于体育运动中捕捉运动员及装备的三维运动，但其外参标定仍成本高昂且耗时。本文提出一种高效、无需工具的多摄像头外参自标定方法，专用于涉及棍状器械（如高尔夫球杆、球棒、冰球杆）的体育运动。该方法利用同步多视角视频中的两类互补信息：(i) 人体关节点（度量尺度未知）和 (ii) 已知长度的刚性棍状器械。我们构建了三阶段优化流程，联合优化相机外参、重建人体与棍状物轨迹，并通过棍长约束确定全局尺度。实验表明，本方法无需专用标定工具即可实现高精度外参标定。为评估此任务，我们发布了首个基于棍状器械体育运动的多摄像头自标定数据集，包含四类运动的合成序列，覆盖3至10个相机。大量实验证明，该方法达到当前最优性能，旋转与平移误差均显著低于现有方法。项目主页：https://fandulu.github.io/sport_stick_multi_cam_calib/。
原文摘要 · Abstract (English)
Multi-camera systems are widely employed in sports to capture the 3D motion of athletes and equipment, yet calibrating their extrinsic parameters remains costly and labor-intensive. We introduce an efficient, tool-free method for multi-camera extrinsic calibration tailored to sports involving stick-like implements (e.g., golf clubs, bats, hockey sticks). Our approach jointly exploits two complementary cues from synchronized multi-camera videos: (i) human body keypoints with unknown metric scale and (ii) a rigid stick-like implement of known length. We formulate a three-stage optimization pipeline that refines camera extrinsics, reconstructs human and stick trajectories, and resolves global scale via the stick-length constraint. Our method achieves accurate extrinsic calibration without dedicated calibration tools. To benchmark this task, we present the first dataset for multi-camera self-calibration in stick-based sports, consisting of synthetic sequences across four sports categories with 3 to 10 cameras. Comprehensive experiments demonstrate that our method delivers SOTA performance, achieving low rotation and translation errors. Our project page: https://fandulu.github.io/sport_stick_multi_cam_calib/.
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