  In recent years, there has been a growing trend of using data-driven methods
in industrial settings. These kinds of methods often process video images or
parts, therefore the integrity of such images is crucial. Sometimes datasets,
e.g. consisting of images, can be sophisticated for various reasons. It becomes
critical to understand how the manipulation of video and images can impact the
effectiveness of a machine learning method. Our case study aims precisely to
analyze the Linemod dataset, considered the state of the art in 6D pose
estimation context. That dataset presents images accompanied by ArUco markers;
it is evident that such markers will not be available in real-world contexts.
We analyze how the presence of the markers affects the pose estimation
accuracy, and how this bias may be mitigated through data augmentation and
other methods. Our work aims to show how the presence of these markers goes to
modify, in the testing phase, the effectiveness of the deep learning method
used. In particular, we will demonstrate, through the tool of saliency maps,
how the focus of the neural network is captured in part by these ArUco markers.
Finally, a new dataset, obtained by applying geometric tools to Linemod, will
be proposed in order to demonstrate our hypothesis and uncovering the bias. Our
results demonstrate the potential for bias in 6DOF pose estimation networks,
and suggest methods for reducing this bias when training with markers.
