基于旋转不变深度层次聚类网络的点云分析

由于解决了三维点云的排列不变性问题，基于三维点云的深度学习方法在计算机三维视觉领域中取得了重大的突破，人们逐渐倾向于使用三维点云来描述物体并基于神经网络结构来提取点云的特征.然而，现有的方法依然无法解决旋转不变性问题，使得目前的模型鲁棒性较差；同时，神经网络结构的设计过于启发式，没有合理利用三维点云的几何结构与分布特性，导致网络结构的表达能力有待提升.鉴于此，提出了一种具有良好兼容性的严格旋转不变性表达以及深度层次类簇网络，试图从理论与实践两个层面解决上述问题.在点云识别、部件分割、语义分割这3个经典任务上进行了旋转鲁棒性对比实验，均取得了最优的效果.

Rotation-invariant Deep Hierarchical Cluster Network for Point Cloud Analysis

In recent years, since the solution of permutation invariance of point cloud, point cloud based deep learning methods have achieved great breakthrough. Point cloud is adopted as input data to describe 3D objects and then neural network is employed to extract features from the point cloud. However, the existing methods cannot solve the rotation-invariance problem, thus existing models are of poor robustness against rotation. Meanwhile, the existing methods merely design the hierarchical structure of neural network by prior knowledge and none of them have made effort to explore the geometric structure underlying the point cloud, which is prone to cause lower capacity of network. For these reasons, a point cloud representation with rotation-invariance and a hierarchical cluster network are proposed, attempting to solve the above two problems in both theoretical and practical ways. Extensive experiments have shown that the proposed method greatly outperforms the state-of-the-arts in rotation robustness on rotation-augmented 3D object classification, object part segmentation, object semantic segmentation benchmarks.