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Existing fiducial markers solutions are designed for efficient detection anddecoding, however, their ability to stand out in natural environments isdifficult to infer from relatively limited analysis. Furthermore, worseningperformance in challenging image capture scenarios - such as poor exposure,motion blur, and off-axis viewing - sheds light on their limitations. E2ETagintroduces an end-to-end trainable method for designing fiducial markers and acomplimentary detector. By introducing back-propagatable marker augmentationand superimposition into training, the method learns to generate markers thatcan be detected and classified in challenging real-world environments using afully convolutional detector network. Results demonstrate that E2ETagoutperforms existing methods in ideal conditions and performs much better inthe presence of motion blur, contrast fluctuations, noise, and off-axis viewingangles. Source code and trained models are available athttps://github.com/jbpeace/E2ETag.

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