Learning with 3D rotations, a hitchhiker's guide to SO(3)

• URL: http://arxiv.org/abs/2404.11735v2
• Date: Wed, 19 Jun 2024 10:17:54 GMT
• Title: Learning with 3D rotations, a hitchhiker's guide to SO(3)
• Authors: A. René Geist, Jonas Frey, Mikel Zobro, Anna Levina, Georg Martius,
• Abstract summary: This paper acts as a survey and guide through rotation representations. By consolidating insights from rotation-based learning, we provide a comprehensive overview of learning functions with rotation representations.
• Score: 17.802455837461125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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