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.
Abstract (translated)
激光雷达(LiDAR)是一种先进的主动遥感技术,基于时间旅行原理,用于捕捉周围环境的高精度三维信息。在研究和发展中,激光雷达受到了广泛关注,预计到2025年,激光雷达产业将达到28亿美元。尽管激光雷达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和巨大的体积,处理激光雷达数据具有挑战性。但是,这样高的分辨率数据在许多应用中具有巨大的潜力,并且在3D物体检测和识别方面具有巨大的潜力。在本研究中,我们提出了基于图神经网络(GNN)的框架,以学习和理解3D激光雷达点云中的物体。GNN是一种深度学习类别,基于图学习原则,通过学习模式和物体,在多种3D计算机视觉任务中取得了成功。
URL
https://arxiv.org/abs/2301.12519
