TopoTag: A Robust and Scalable Topological Fiducial Marker System

Guoxing Yu    Yongtao Hu    Jingwen Dai

Guangdong Virtual Reality Technology Co., Ltd. (aka. Ximmerse), Guangzhou

IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG 2021)

Abstract

Fiducial markers have been playing an important role in augmented reality (AR), robot navigation, and general applications where the relative pose between a camera and an object is required. Here we introduce TopoTag, a robust and scalable topological fiducial marker system, which supports reliable and accurate pose estimation from a single image. TopoTag uses topological and geometrical information in marker detection to achieve higher robustness. Topological information is extensively used for 2D marker detection, and further corresponding geometrical information for ID decoding. Robust 3D pose estimation is achieved by taking advantage of all TopoTag vertices. Without sacrificing bits for higher recall and precision like previous systems, TopoTag can use full bits for ID encoding. TopoTag supports tens of thousands unique IDs and easily extends to millions of unique tags resulting in massive scalability. We collected a large test dataset including in total 169,713 images for evaluation, involving in-plane and out-of-plane rotation, image blur, different distances and various backgrounds, etc. Experiments on the dataset and real indoor and outdoor scene tests with a rolling shutter camera both show that TopoTag significantly outperforms previous fiducial marker systems in terms of various metrics, including detection accuracy, vertex jitter, pose jitter and accuracy, etc. In addition, TopoTag supports occlusion as long as the main tag topological structure is maintained and allows for flexible shape design where users can customize internal and external marker shapes. Code for our marker design/generation, marker detection, and dataset are available at https://herohuyongtao.github.io/research/publications/topo-tag/.

Paper

Code / Tools

Result

  • Comparison of dictionary size, tracking range and detection accuracy
  • Comparison of pose error and jitter under laboratory test with a global shutter industrial camera (best results are shown in bold and underlined)
  • Comparison of pose jitter under real scene test with a rolling shutter camera (“---” means detection failure, best results are shown in bold and underlined)

Dataset

Bibtex

@article{yu2021topotag,
  title={{TopoTag: A Robust and Scalable Topological Fiducial Marker System}},
  author={Yu, Guoxing and Hu, Yongtao and Dai, Jingwen},
  journal={IEEE Transactions on Visualization and Computer Graphics (TVCG)},
  volume={27},
  number={9},
  pages={3769-3780},
  year={2021},
  publisher={IEEE}
}