mlsanityData to 14 Oct 2026

FMAC: a Fair Fiducial Marker Accuracy Comparison Software

arXiv 2601.07723 ↗First posted 12 Jan 2026cs.CV cs.ROcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 33 references that are in the map. First posted on arXiv 12 Jan 2026.

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4 of 33 references.

References · 4

  1. 2021DeepTag: A General Framework for Fiducial Marker Design and Detectioncited by 8
  2. 2017STag: A Stable Fiducial Marker Systemcited by 6
  3. 2019TopoTag: A Robust and Scalable Topological Fiducial Marker Systemcited by 6
  4. 2020Radial Coverage Strength for Optimization of Multi-Camera Deploymentcited by 1

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