3D Object Detection in LiDAR Point Clouds using Graph Neural Networks

TL;DR

This paper introduces a novel Graph Neural Network framework designed to improve 3D object detection in LiDAR point clouds, addressing the challenges of processing high-resolution 3D data for autonomous vehicle applications.

Contribution

The paper presents a new GNN-based approach specifically tailored for 3D LiDAR data, enhancing object detection accuracy in complex 3D environments.

Findings

Effective detection of objects in LiDAR point clouds

Improved accuracy over traditional methods

Potential for real-time applications

Abstract

LiDAR (Light Detection and Ranging) is an advanced active remote sensing technique working on the principle of time of travel (ToT) for capturing highly accurate 3D information of the surroundings. LiDAR has gained wide attention in research and development with the LiDAR industry expected to reach 2.8 billion $ by 2025. Although the LiDAR dataset is of rich density and high spatial resolution, it is challenging to process LiDAR data due to its inherent 3D geometry and massive volume. But such a high-resolution dataset possesses immense potential in many applications and has great potential in 3D object detection and recognition. In this research we propose Graph Neural Network (GNN) based framework to learn and identify the objects in the 3D LiDAR point clouds. GNNs are class of deep learning which learns the patterns and objects based on the principle of graph learning which have shown success in various 3D computer vision tasks.