  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.
