PointNet

Overview

The PointNet architecture is a foundational model for processing point cloud data. Although it was invented in 2016, its implementation is powerful, efficient, and possesses many desirable properties for working with point cloud data compared to other techniques like voxel grids or 2D image projection. The PointNet design provides a generic framework that supports classification, 3D object detection, point normals prediction, parts segmentation, semantic scene segmentation, and more. First, we introduce the PointNet architecture, followed by training an example implementation on a toy example.

Machine learning for point clouds

Generally speaking, we treat point clouds as a sequence of $n$ points $(x, y, z) \isin \mathbb{R}^3$ in a Euclidean space. The coordinates are treated as features, but they can optionally include any number of arbitrary features, such as normals, colors, density, etc.

Working ...