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.

本文通过分析 Linemod 数据集中伴随 ArUco 标记的图像噪声，探讨其对于机器学习算法 6DOF 姿态估计的影响，并提出了通过数据增强和其他方法减少偏差的方案。同时，本文证明了标记对于神经网络实现 6DOF 姿态估计具有一定的影响因素。

揭示基于 RGB 的 6-DoF 目标位姿估计中的背景偏差

Uncovering the Background-Induced bias in RGB based 6-DoF Object Pose  Estimation

We propose a simple and efficient method for exploiting synthetic images when
training a Deep Network to predict a 3D pose from an image. The ability of
using synthetic images for training a Deep Network is extremely valuable as it
is easy to create a virtually infinite training set made of such images, while
capturing and annotating real images can be very cumbersome. However, synthetic
images do not resemble real images exactly, and using them for training can
result in suboptimal performance. It was recently shown that for exemplar-based
approaches, it is possible to learn a mapping from the exemplar representations
of real images to the exemplar representations of synthetic images. In this
paper, we show that this approach is more general, and that a network can also
be applied after the mapping to infer a 3D pose: At run time, given a real
image of the target object, we first compute the features for the image, map
them to the feature space of synthetic images, and finally use the resulting
features as input to another network which predicts the 3D pose. Since this
network can be trained very effectively by using synthetic images, it performs
very well in practice, and inference is faster and more accurate than with an
exemplar-based approach. We demonstrate our approach on the LINEMOD dataset for
3D object pose estimation from color images, and the NYU dataset for 3D hand
pose estimation from depth maps. We show that it allows us to outperform the
state-of-the-art on both datasets.

该研究提出了一种基于合成图像的深度网络训练方法，通过使用基于 “范例” 的映射，将合成图像的特征空间映射到实际图像的特征空间中，以此用于 3D 姿态估计的预测。通过实验，该方法在 LINEMOD 数据集中实现了优于现有方法的性能表现。