Representation Learning for Point Cloud Understanding

TL;DR

This paper explores advanced methods for learning representations from 3D point cloud data, integrating 2D pre-trained models to enhance understanding in applications like autonomous driving and robotics.

Contribution

It introduces novel techniques combining supervised, self-supervised, and transfer learning approaches, leveraging 2D models to improve 3D point cloud understanding.

Findings

Significant improvement in 3D understanding accuracy

Effective transfer of 2D knowledge to 3D tasks

Validated methods through extensive experiments

Abstract

With the rapid advancement of technology, 3D data acquisition and utilization have become increasingly prevalent across various fields, including computer vision, robotics, and geospatial analysis. 3D data, captured through methods such as 3D scanners, LiDARs, and RGB-D cameras, provides rich geometric, shape, and scale information. When combined with 2D images, 3D data offers machines a comprehensive understanding of their environment, benefiting applications like autonomous driving, robotics, remote sensing, and medical treatment. This dissertation focuses on three main areas: supervised representation learning for point cloud primitive segmentation, self-supervised learning methods, and transfer learning from 2D to 3D. Our approach, which integrates pre-trained 2D models to support 3D network training, significantly improves 3D understanding without merely transforming 2D data. Extensive experiments validate the effectiveness of our methods, showcasing their potential to advance point cloud representation learning by effectively integrating 2D knowledge.