Learning with 3D rotations, a hitchhiker's guide to SO(3)
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要