无需标定板和运动限制，用三焦点张量实现高精度相机自标定。
Tensor-Based Self-Calibration of Cameras via the TrifocalCalib Method
- 基于校准三焦点张量构建方程，仅需最少图像数据完成自标定。
- 在合成与真实数据集上均优于现有学习与经典方法，精度显著提升。
- 适合自动驾驶等需实时适应的场景，代码开源便于复现。
在无先验场景知识条件下估计相机内参是计算机视觉中的基础挑战，对自动驾驶与车队协同等应用尤为重要。本文提出基于校准三焦点张量的一组新方程，实现从极简图像数据出发的投影相机自标定。所提方法TrifocalCalib在准确性和鲁棒性上显著优于近期学习型与经典方法。本方法无需标定板，不限制相机运动模式，可同时估计焦距与主点坐标。在程序生成的合成环境及结构化数据集上的评估验证了其有效性。为支持可复现性，代码已公开。
原文摘要 · Abstract (English)
Estimating camera intrinsic parameters without prior scene knowledge is a fundamental challenge in computer vision. This capability is particularly important for applications such as autonomous driving and vehicle platooning, where precalibrated setups are impractical and real-time adaptability is necessary. To advance the state-of-the-art, we present a set of equations based on the calibrated trifocal tensor, enabling projective camera self-calibration from minimal image data. Our method, termed TrifocalCalib, significantly improves accuracy and robustness compared to both recent learning-based and classical approaches. Unlike many existing techniques, our approach requires no calibration target, imposes no constraints on camera motion, and simultaneously estimates both focal length and principal point. Evaluations in both procedurally generated synthetic environments and structured dataset-based scenarios demonstrate the effectiveness of our approach. To support reproducibility, we make the code publicly available.
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