1 Introduction  
2 Preliminaries  
3 A Taxonomy of Geometrical Prior Knowledge for Deep Neural Networks  
4 Architecture Constraints  
5 Training Procedure  
6 Discovering In- & Equivariance from Data  
7 Measuring Equivariance  
8 Datasets and Benchmarks  
9 Towards Geometrical Prior Knowledge for Autonomous Driving  
10 Conclusion