论文标题
3D点云的旋转不变的本地到全球表示学习
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud
论文作者
论文摘要
我们为3D点云数据提出了一种局部到全球表示学习算法，该算法适用于处理各种几何变换，尤其是旋转，而无需相对于转换的明确数据增强。我们的模型利用基于图形卷积神经网络的多级抽象，该卷积神经网络构建了描述符层次结构，以自下而上的方式编码输入对象的旋转不变形状信息。每个级别中的描述符是从基于图表的神经网络获得的，这是通过3D点的随机采样来获得的，这有效地使学习的表示对输入数据的变化有牢固。提出的算法在旋转增强的3D对象识别和分割基准上提出了最新性能，我们通过全面的消融实验进一步分析了其特征。
We propose a local-to-global representation learning algorithm for 3D point cloud data, which is appropriate to handle various geometric transformations, especially rotation, without explicit data augmentation with respect to the transformations. Our model takes advantage of multi-level abstraction based on graph convolutional neural networks, which constructs a descriptor hierarchy to encode rotation-invariant shape information of an input object in a bottom-up manner. The descriptors in each level are obtained from a neural network based on a graph via stochastic sampling of 3D points, which is effective in making the learned representations robust to the variations of input data. The proposed algorithm presents the state-of-the-art performance on the rotation-augmented 3D object recognition and segmentation benchmarks, and we further analyze its characteristics through comprehensive ablative experiments.
