We propose a general method for deep learning based point cloud analysis, which is invariant to rotation on the inputs. Classical methods are vulnerable to rotation, as they usually take aligned point clouds as input. Principle Component Analysis (PCA) is a practical approach to achieve rotation invariance. However, there are still some gaps between theory and practical algorithms. In this work, we present a thorough study on designing rotation invariant algorithms for point cloud analysis. We first formulate it as a permutation invariant problem, then propose a general framework which can be combined with any backbones. Our method is beneficial for further research such as 3D pre-training and multi-modal learning. Experiments show that our method has considerable or better performance compared to state-of-the-art approaches on common benchmarks. Code is available at https://github.com/luoshuqing2001/RI_framework.

我们提出了一种基于深度学习的点云分析通用方法，对输入具有旋转不变性。通过将其作为置换不变问题进行公式化，并提出了一个通用框架，可与任何骨干网络结合使用，我们的方法在常见基准测试中相较于最先进的方法表现出显著或更好的性能，对于3D预训练和多模态学习等进一步研究非常有益。

旋转不变点云分析的通用框架