Compared to 2D images, 3D point clouds are much more sensitive to rotations. We expect the point features describing certain patterns to keep invariant to the rotation transformation. There are many recent SOTA works dedicated to rotation-invariant learning for 3D point clouds. However, current rotation-invariant methods lack generalizability on the point clouds in the open scenes due to the reliance on the global distribution, \ie the global scene and backgrounds. Considering that the output activation is a function of the pattern and its orientation, we need to eliminate the effect of the orientation.In this paper, inspired by the idea that the network weights can be considered a set of points distributed in the same 3D space as the input points, we propose Weight-Feature Alignment (WFA) to construct a local Invariant Reference Frame (IRF) via aligning the features with the principal axes of the network weights. Our WFA algorithm provides a general solution for the point clouds of all scenes. WFA ensures the model achieves the target that the response activity is a necessary and sufficient condition of the pattern matching degree. Practically, we perform experiments on the point clouds of both single objects and open large-range scenes. The results suggest that our method almost bridges the gap between rotation invariance learning and normal methods.

翻译：与二维图像相比，三维点云对旋转更为敏感。我们期望描述特定模式的点特征能够对旋转变换保持不变。近期有许多优秀工作致力于三维点云的旋转不变性学习。然而，当前旋转不变性方法由于依赖全局分布（即全局场景和背景），在开放场景的点云上缺乏通用性。考虑到输出激活是模式及其朝向的函数，我们需要消除朝向的影响。本文受网络权重可视为与输入点分布于同一三维空间的点集这一思路启发，提出权重-特征对齐（WFA）方法，通过将特征与网络权重的主轴对齐来构建局部不变参考系（IRF）。我们的WFA算法为所有场景的点云提供了通用解决方案。WFA确保模型达成响应活动与模式匹配度互为充分必要条件的目标。在实际应用中，我们在单个物体和开放大范围场景的点云上进行了实验。结果表明，我们的方法几乎弥合了旋转不变性学习与常规方法之间的差距。