  Deep Neural Networks achieve state-of-the-art results in many different
problem settings by exploiting vast amounts of training data. However,
collecting, storing and - in the case of supervised learning - labelling the
data is expensive and time-consuming. Additionally, assessing the networks'
generalization abilities or predicting how the inferred output changes under
input transformations is complicated since the networks are usually treated as
a black box. Both of these problems can be mitigated by incorporating prior
knowledge into the neural network. One promising approach, inspired by the
success of convolutional neural networks in computer vision tasks, is to
incorporate knowledge about symmetric geometrical transformations of the
problem to solve that affect the output in a predictable way. This promises an
increased data efficiency and more interpretable network outputs. In this
survey, we try to give a concise overview about different approaches that
incorporate geometrical prior knowledge into neural networks. Additionally, we
connect those methods to 3D object detection for autonomous driving, where we
expect promising results when applying those methods.
