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Follow along with this post using the Google Colab notebook.

In todays rapidly evolving technological landscape, 3D technology is becoming indispensable. Prototyping, virtual try-on, virtual and augmented reality experiences, digital twins, surveying, medical prosthetics, and the film and gaming industries are just the tip of the 3D iceberg. LinkedIn estimates that the worldwide demand for 3D content will surpass $3 billion by 2028, showing no signs of deceleration. From Frozen to Fortnite, its safe to say that 3D models are becoming the new photographs.

As demand for 3D data grows, so does the need for effective methods to classify and understand 3D data. PointNet, invented in 2016 by Stanford researchers, is a fossil in the fast-paced world of ML, and yet it has withstood the test of time. As recently as 2023, researchers have published variants on the PointNet architecture for tasks as diverse as:

Designed specifically to grapple with the complexities inherent in 3D point cloud data, PointNet offers a robust and versatile solution in an era where the utilization of 3D data is more prevalent than ever before.

To aid us on our PointNet journey, we will use PyTorch3D. PyTorch3D, from Facebook AI Research (FAIR), is a flexible and efficient 3D deep learning tasks framework that empowers researchers and practitioners to delve into the intricacies of 3D machine learning. With its rich toolset, we can visualize and manipulate 3D data to build a 3D object classification model with PointNet. If you seek more information on PointNet, PyTorch3D, and 3D machine learning, you may be interested in my new Educative course 3D Machine Learning with PyTorch3D.

Since its creation, PointNet has become a cornerstone in 3D deep learning for efficient point cloud data processing. But exactly what is a point cloud?

Original post:

Point Cloud Classification with PointNet and PyTorch3D | by Mason McGough | Mar, 2024 - Towards Data Science