PointNet: The First Neural Network to Handle Directly 3D Point Clouds

Get Up to Speed on 3D Deep Learning With PointNet

3D Point Cloud of an Airplane — Image by author

PointNet is a deep learning network architecture proposed in 2016 by Stanford researchers and is the first neural network to handle directly 3D point clouds.

In this article, I explain how PointNet works after reimplementing it with PyTorch.

You can see the final result in the video below:

Like the original paper, we use the ShapeNet dataset for this project, which contains 16,881 shapes from 16 categories.

After discussing briefly what a convolutional neural network is, we will explain the architecture of PointNet.

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