Point cloud analysis is a fundamental task in 3D computer vision. Most
previous works have conducted experiments on synthetic datasets with
well-aligned data; while real-world point clouds are often not pre-aligned. How
to achieve rotation invariance remains an open problem in point cloud analysis.
To meet this challenge, we propose a novel approach toward achieving
rotation-invariant (RI) representations by combining local geometry with global
topology. In our local-global-representation (LGR)-Net, we have designed a
two-branch network where one stream encodes local geometric RI features and the
other encodes global topology-preserving RI features. Motivated by the
observation that local geometry and global topology have different yet
complementary RI responses in varying regions, two-branch RI features are fused
by an innovative multi-layer perceptron (MLP) based attention module. To the
best of our knowledge, this work is the first principled approach toward
adaptively combining global and local information under the context of RI point
cloud analysis. Extensive experiments have demonstrated that our LGR-Net
achieves the state-of-the-art performance on various rotation-augmented
versions of ModelNet40, ShapeNet, ScanObjectNN, and S3DIS.

本文介绍了一种新的方法来实现点云数据的旋转不变性，使用局部几何特征和全局拓扑特征相结合的局部 - 全局表示网络。该网络使用多层感知器做注意力机制来融合两种旋转不变性特征，并在模型识别任务上取得了最先进的效果。