用高保真合成图像公平比较标记点的位姿估计精度
FMAC: a Fair Fiducial Marker Accuracy Comparison Software
- 基于物理渲染生成大量高保真合成图像，覆盖6自由度空间
- 通过36种组合可视化各自由度与位姿误差的相关性
- 开源工具支持对常见标记点的精度评估，适合视觉定位研究者
本文提出一种公平比较基于标识标记点位姿估计精度的方法。该方法依赖大规模高保真合成图像，实现对6个自由度的深入探索。通过低差异采样，可绘制出36种自由度组合下的位姿误差相关性图。图像采用专门开发的基于物理的光线追踪代码生成，直接使用任意相机的标准标定参数，准确还原图像畸变、散焦和衍射模糊。同时对锐利边缘应用亚像素采样以提升渲染保真度。在介绍渲染算法及其实验验证后，论文提出一种位姿精度评估方法，并应用于多个知名标记点，揭示其在位姿估计中的优劣。代码已开源，发布于GitHub。
原文摘要 · Abstract (English)
This paper presents a method for carrying fair comparisons of the accuracy of pose estimation using fiducial markers. These comparisons rely on large sets of high-fidelity synthetic images enabling deep exploration of the 6 degrees of freedom. A low-discrepancy sampling of the space allows to check the correlations between each degree of freedom and the pose errors by plotting the 36 pairs of combinations. The images are rendered using a physically based ray tracing code that has been specifically developed to use the standard calibration coefficients of any camera directly. The software reproduces image distortions, defocus and diffraction blur. Furthermore, sub-pixel sampling is applied to sharp edges to enhance the fidelity of the rendered image. After introducing the rendering algorithm and its experimental validation, the paper proposes a method for evaluating the pose accuracy. This method is applied to well-known markers, revealing their strengths and weaknesses for pose estimation. The code is open source and available on GitHub.
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