3D Object Detection in LiDAR Point Clouds using Graph Neural Networks
This addresses the challenge of processing LiDAR data for applications like autonomous driving, but it appears incremental as GNNs are already used in 3D computer vision.
The researchers tackled 3D object detection in LiDAR point clouds by proposing a Graph Neural Network (GNN) framework, which learns and identifies objects in this high-resolution data.
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.