论文标题
旋转不变点云分析的深度位置和关系特征学习
Deep Positional and Relational Feature Learning for Rotation-Invariant Point Cloud Analysis
论文作者
论文摘要
在本文中，我们提出了一个用于点云分析的旋转不变的深网。基于点的深网通常设计为识别基于点坐标的大致对齐的3D形状，但遭受形状旋转的性能下降。某些几何特征，例如，点作为网络输入的点的距离和角度，是旋转不变的，但失去了点的位置信息。在这项工作中，我们通过将点的位置信息作为输入，同时产生旋转不变性，为点云提出了一个新颖的深网络。该网络是分层的，依赖两个模块：位置功能嵌入块和一个关系功能嵌入块。在处理点云作为输入时，模块和整个网络都被证明是旋转不变的。实验显示了基准数据集上的最新分类和分割性能，而消融研究表明了网络设计的有效性。
In this paper we propose a rotation-invariant deep network for point clouds analysis. Point-based deep networks are commonly designed to recognize roughly aligned 3D shapes based on point coordinates, but suffer from performance drops with shape rotations. Some geometric features, e.g., distances and angles of points as inputs of network, are rotation-invariant but lose positional information of points. In this work, we propose a novel deep network for point clouds by incorporating positional information of points as inputs while yielding rotation-invariance. The network is hierarchical and relies on two modules: a positional feature embedding block and a relational feature embedding block. Both modules and the whole network are proven to be rotation-invariant when processing point clouds as input. Experiments show state-of-the-art classification and segmentation performances on benchmark datasets, and ablation studies demonstrate effectiveness of the network design.
