DeepFormableTag

Yaldiz, Mustafa B.; Meuleman, Andreas; Jang, Hyeonjoong; Ha, Hyunho; Kim, Min H.

doi:10.1145/3450626.3459762

Cited by 10 publications

(1 citation statement)

References 43 publications

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“…As the field of fiducial markers continues to evolve, deep learning has emerged as a promising technique to address some of the existing challenges. Leveraging neural networks, researchers are exploring ways to enhance the detection and tracking capabilities of camera systems, even in complex scenarios characterized by occlusions or variable lighting [ 20 , 21 ]. Despite its potential, applying deep learning in marker detection is not without its own challenges.…”

Section: Related Workmentioning

confidence: 99%

Fiducial Objects: Custom Design and Evaluation

García-Ruiz,

Romero-Ramirez,

Muñoz-Salinas

et al. 2023

Sensors

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Camera pose estimation is vital in fields like robotics, medical imaging, and augmented reality. Fiducial markers, specifically ArUco and Apriltag, are preferred for their efficiency. However, their accuracy and viewing angle are limited when used as single markers. Custom fiducial objects have been developed to address these limitations by attaching markers to 3D objects, enhancing visibility from multiple viewpoints and improving precision. Existing methods mainly use square markers on non-square object faces, leading to inefficient space use. This paper introduces a novel approach for creating fiducial objects with custom-shaped markers that optimize face coverage, enhancing space utilization and marker detectability at greater distances. Furthermore, we present a technique for the precise configuration estimation of these objects using multiviewpoint images. We provide the research community with our code, tutorials, and an application to facilitate the building and calibration of these objects. Our empirical analysis assesses the effectiveness of various fiducial objects for pose estimation across different conditions, such as noise levels, blur, and scale variations. The results suggest that our customized markers significantly outperform traditional square markers, marking a positive advancement in fiducial marker-based pose estimation methods.

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Section: Related Workmentioning

confidence: 99%