Learning with 3D rotations, a hitchhiker's guide to SO(3)
A. René Geist Jonas Frey Mikel Zobro Anna Levina Georg Martius
Many settings in machine learning require the selection of a rotation
representation. However, choosing a suitable representation from the many
available options is challenging. This paper acts as a survey and guide through
rotation representations. We walk through their properties that harm or benefit
deep learning with gradient-based optimization. By consolidating insights from
rotation-based learning, we provide a comprehensive overview of learning
functions with rotation representations. We provide guidance on selecting
representations based on whether rotations are in the model's input or output
and whether the data primarily comprises small angles.