PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

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Introduction

This paper builds off of ideas from PointNet (Qi et al., 2017).

PointNet and PointNet++ are deep neural network architecture that take point clouds as their input and perform the tasks such as classification or part segmentation or scene segmentation.


PointNet differs from other point cloud processing networks by directly taking the unordered points from the point cloud as input, instead of re-representing the point cloud by voxelization or 2D images.


Point cloud torus


Examples of point clouds and their associated task. Classification (left), part segmentation (centre), scene segmentation (right)

Review of PointNet

PointNet architecture
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PointNet++

Problem Statement

Method

PointNet++ architecture
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Sampling Layer

Grouping Layer

Example of the two ways to perform grouping
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PointNet Layer

Robust Feature Learning under Non-Uniform Sampling Density

Experiments

Sources

1. Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space, 2017

2. Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation, 2017