What is it about?

This study provides a comprehensive systematic review of artificial (fiducial) markers, analyzing 88 articles selected through a rigorous snowballing methodology. The research organizes the current state of the art into a detailed taxonomy that categorizes marker characteristics into two primary groups: Intrinsic Characteristics: Physical and visual aspects, including morphology (layout and visual elements), encoding methods, dimensions, chrominance, and materials (ranging from standard paper to rigid materials like steel and wood). Extrinsic Characteristics: Behavioral and operational aspects, focusing on various types of robustness (to occlusion, lighting variations, blur, and challenging viewing angles) and the diverse detection algorithms employed. The primary outcome is a theoretical framework that provides best practices, guiding researchers and practitioners in designing or selecting robust fiducial markers tailored to their specific applications.

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Why is it important?

While fiducial markers are foundational to technologies such as augmented reality, robotics, and computer-assisted surgery, designing a truly robust system and reliable under real-world conditions remains an arduous task. Previous reviews often lacked a comprehensive list of available markers or limited their scope to specific application domains. This study addresses these gaps by providing a formal definition of fiducial markers and mapping how specific design choices—such as morphology or material—directly impact detection accuracy and performance. The framework helps developers balance technical performance with the practical processing limitations of hardware, such as smartphones, drones, or robots.

Perspectives

With this study, our goal was to provide a structured roadmap for researchers and developers navigating the specialized field of artificial (fiducial) marker systems. We observed that while markers are becoming more resilient through AI-based detection, choosing the right design is still a challenge. Our framework serves as a guide for best practices, helping users comprehend the characteristics that contribute to a robust system. We hope this work facilitates better decision-making and identifies clear gaps for future enhancements in the design and performance of fiducial systems.

prof. Carlos Gustavo Resque dos Santos
Universidade Federal do Para

Writing this paper revealed that fiducial markers are the anchors of trust in an automated world. More than just pixels, they provide the certainty required for machines to safely navigate our environment. I hope that this framework transforms how we design these systems, ensuring that as computer vision permeates critical sectors like healthcare and industry, it does so with absolute reliability. I wish for this work to help build a safer world where the bond between human needs and machine perception is unbreakable.

Professor Benedito De Souza Ribeiro Neto
Instituto Federal de Educacao Ciencia e Tecnologia do Para

Read the Original

This page is a summary of: Artificial Markers: A Comprehensive Systematic Review and Design Framework, ACM Computing Surveys, January 2026, ACM (Association for Computing Machinery),
DOI: 10.1145/3793661.
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