# E2ETag: An End-to-End Trainable Method for Generating and Detecting Fiducial Markers

arXiv: https://arxiv.org/abs/2105.14184 · HTML: https://mlsanity.com/paper/2105.14184/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 29 May 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [ChromaTag: A Colored Marker and Fast Detection Algorithm](https://mlsanity.com/paper/1708.02982/) (2017)

## Built on by (most cited first, 7 of 7)

- [Neural Lens Modeling](https://mlsanity.com/paper/2304.04848/) (2023)
- [NeuralMarker: A Framework for Learning General Marker Correspondence](https://mlsanity.com/paper/2209.08896/) (2022)
- [DeepFormableTag: End-to-end Generation and Recognition of Deformable Fiducial Markers](https://mlsanity.com/paper/2206.08026/) (2022)
- [CylinderTag: An Accurate and Flexible Marker for Cylinder-Shape Objects Pose Estimation Based on Projective Invariants](https://mlsanity.com/paper/2310.13320/) (2023)
- [YoloTag: Vision-based Robust UAV Navigation with Fiducial Markers](https://mlsanity.com/paper/2409.02334/) (2024)
- [Uncovering the Metaverse within Everyday Environments: a Coarse-to-Fine Approach](https://mlsanity.com/paper/2404.17587/) (2024)
- [Ninja Codes: Neurally Generated Fiducial Markers for Stealthy 6-DoF Tracking](https://mlsanity.com/paper/2510.18976/) (2025)

## Abstract (arXiv)

Existing fiducial markers solutions are designed for efficient detection and decoding, however, their ability to stand out in natural environments is difficult to infer from relatively limited analysis. Furthermore, worsening performance in challenging image capture scenarios - such as poor exposure, motion blur, and off-axis viewing - sheds light on their limitations. E2ETag introduces an end-to-end trainable method for designing fiducial markers and a complimentary detector. By introducing back-propagatable marker augmentation and superimposition into training, the method learns to generate markers that can be detected and classified in challenging real-world environments using a fully convolutional detector network. Results demonstrate that E2ETag outperforms existing methods in ideal conditions and performs much better in the presence of motion blur, contrast fluctuations, noise, and off-axis viewing angles. Source code and trained models are available at https://github.com/jbpeace/E2ETag.