  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.
