Zuhao Zou, Xiangcheng Hu, Jianhao Jiao, Jin Wu · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.28571 · cited by 0 works (OpenAlex) · open-access PDF
ScreenCalib专门用于光检测和差距(LiDAR) - 摄像头间接校准使用普通可编程显示器,提供已知的视觉标记和控制的几何,同时减少专门的目标准备。摄像头观察显示的AprilTags,而LiDAR测量物理屏幕表面和边界,需要间接的跨模式关联。显示地理学只需要一个静态的排列模糊,而室内混乱使提取变得复杂。ScreenCalib通过标签引导的显示提取,地理假设生成和精简,以及周围的场景边缘进行模糊分辨率。单位置模式只需要一个静态的排列模糊。共同的多位置模式通过计算机清洁共识结合了独立的静态观察,并联合地
Machine translation (Meta M2M100); the English original follows.
ScreenCalib enables light detection and ranging (LiDAR)--camera extrinsic calibration using an ordinary programmable display, providing known visual markers and controlled geometry while reducing dedicated target preparation. The camera observes displayed AprilTags, whereas the LiDAR measures the physical screen surface and boundary, requiring indirect cross-modal association. Display geometry introduces association ambiguities, while indoor clutter complicates extraction. ScreenCalib addresses these challenges through tag-guided display extraction, geometric hypothesis generation and refinement, and surrounding scene edges for ambiguity resolution. The single-pose mode requires only one static placement. The joint multi-pose mode combines independent static observations through cross-pose consensus and jointly refines the shared extrinsics and tag scale, reducing sensitivity to tag-size measurement errors without requiring continuous motion or inter-pose trajectory estimation. Experiments in simulation and on three different sensor platforms cover mechanical and nonrepetitive LiDARs in clean and cluttered environments. Benchmark results show that ScreenCalib outperforms competitive calibration-board-based methods in translation accuracy. Joint multi-pose refinement further reduces mean rotation and translation errors across the three platforms and improves rendered-map alignment. Evaluation across readily available displays, including cell phones, tablets, laptops, computer monitors, and televisions, further examines ScreenCalib's versatility as a practical alternative to dedicated calibration targets. The source code is available at https://github.com/ZuhaoZou2026/ScreenCalib.
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Topics: Computer science, Clutter, Computer vision, Calibration, Ranging, Artificial intelligence
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