Mini-PointNetPlus: a local feature descriptor in deep learning model for 3d environment perception

TL;DR

Mini-PointNetPlus introduces a novel local feature descriptor that enhances 3D environment perception by fully utilizing point cloud data, outperforming traditional max-pooling based methods.

Contribution

It proposes mini-PointNetPlus as a plug-and-play alternative to PointNet, addressing information loss and improving 3D perception accuracy.

Findings

Significant performance improvement demonstrated in experiments.

Mini-PointNetPlus outperforms traditional PointNet.

The vanilla PointNet is a special case of mini-PointNetPlus.

Abstract

Common deep learning models for 3D environment perception often use pillarization/voxelization methods to convert point cloud data into pillars/voxels and then process it with a 2D/3D convolutional neural network (CNN). The pioneer work PointNet has been widely applied as a local feature descriptor, a fundamental component in deep learning models for 3D perception, to extract features of a point cloud. This is achieved by using a symmetric max-pooling operator which provides unique pillar/voxel features. However, by ignoring most of the points, the max-pooling operator causes an information loss, which reduces the model performance. To address this issue, we propose a novel local feature descriptor, mini-PointNetPlus, as an alternative for plug-and-play to PointNet. Our basic idea is to separately project the data points to the individual features considered, each leading to a permutation invariant. Thus, the proposed descriptor transforms an unordered point cloud to a stable order. The vanilla PointNet is proved to be a special case of our mini-PointNetPlus. Due to fully utilizing the features by the proposed descriptor, we demonstrate in experiment a considerable performance improvement for 3D perception.