While Deep Neural Networks (DNNs) achieve state-of-the-art results in many
different problem settings, they are affected by some crucial weaknesses. On
the one hand, DNNs depend on exploiting a vast amount of training data, whose
labeling process is time-consuming and expensive. On the other hand, DNNs are
often treated as black box systems, which complicates their evaluation and
validation. Both problems can be mitigated by incorporating prior knowledge
into the DNN.
One promising field, inspired by the success of convolutional neural networks
(CNNs) in computer vision tasks, is to incorporate knowledge about symmetric
geometrical transformations of the problem to solve. This promises an increased
data-efficiency and filter responses that are interpretable more easily. In
this survey, we try to give a concise overview about different approaches to
incorporate geometrical prior knowledge into DNNs. Additionally, we try to
connect those methods to the field of 3D object detection for autonomous
driving, where we expect promising results applying those methods.Comment: Survey Pape