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.

翻译：LiDAR（光检测与测距）是一种先进的主动遥感技术，基于飞行时间（ToT）原理捕获周围环境的高精度三维信息。LiDAR已在研究与开发领域获得广泛关注，预计LiDAR产业到2025年将达到28亿美元。尽管LiDAR数据集具有丰富的密度和高空间分辨率，但由于其固有的三维几何结构和庞大的数据量，处理LiDAR数据仍具挑战性。然而，这种高分辨率数据集在许多应用中具有巨大潜力，尤其在三维目标检测与识别方面。本研究提出基于图神经网络（GNN）的框架，用于学习和识别三维LiDAR点云中的目标。GNN是一类基于图学习原理来学习模式和目标的深度学习方法，已在多项三维计算机视觉任务中取得成功。