本文为美国斯坦福大学（作者：Charles R. Qi）的毕业论文。

这项工作基于我们的arXiv技术报告，该报告出现在2017年CVPR上。我们提出了一种新的点云（无序点集）深网体系结构。您也可以查看我们的项目网页，以获得更深入的介绍。

点云是一种重要的几何数据结构。由于其格式不规则，大多数研究人员将这些数据转换为规则的三维体素网格或图像集合。然而，这会使数据变得不必要的庞大，并导致问题。本文设计了一种新的直接使用点云的神经网络，它很好地满足了输入点的排列不变性。我们的网络名为PointNet，为从对象分类、部件分割到场景语义分析的应用程序提供了一个统一的体系结构。虽然简单，但PointNet非常高效。

在这个存储库中，我们发布了代码和数据，用于从三维形状采样的点云上训练PointNet分类网络，以及在ShapeNet零件数据集上训练零件分割网络。

This work is based on our arXiv tech report, which is going to appear in CVPR 2017. We proposed a novel deep net architecture for point clouds (as unordered point sets). You can also check our project webpage for a deeper introduction.

Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Though simple, PointNet is highly efficient and effective.

In this repository, we release code and data for training a PointNet classification network on point clouds sampled from 3D shapes, as well as for training a part segmentation network on ShapeNet Part dataset.

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