In this paper, we present new feature encoding methods for Detection of 3D objects in point clouds. We used a graph neural network (GNN) for Detection of 3D objects namely cars, pedestrians, and cyclists. Feature encoding is one of the important steps in Detection of 3D objects. The dataset used is point cloud data which is irregular and unstructured and it needs to be encoded in such a way that ensures better feature encapsulation. Earlier works have used relative distance as one of the methods to encode the features. These methods are not resistant to rotation variance problems in Graph Neural Networks. We have included angular-based measures while performing feature encoding in graph neural networks. Along with that, we have performed a comparison between other methods like Absolute, Relative, Euclidean distances, and a combination of the Angle and Relative methods. The model is trained and evaluated on the subset of the KITTI object detection benchmark dataset under resource constraints. Our results demonstrate that a combination of angle measures and relative distance has performed better than other methods. In comparison to the baseline method(relative), it achieved better performance. We also performed time analysis of various feature encoding methods.
Panther: A Cost-Effective Privacy-Preserving Framework for GNN Training and Inference Services in Cloud Environments
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；College of Information Engineering, Shanghai Maritime University（上海 Maritime University 信息工程学院）
3D Object Detection in LiDAR Point Clouds using Graph Neural Networks
LiDAR（Light Detection and Ranging）是一种先进的主动遥感技术，其工作原理是旅行时间（ToT），用于捕获周围环境的高精度3D信息。LiDAR在研发方面获得了广泛关注，预计到2025年LiDAR产业将达到28亿美元。虽然LiDAR数据集具有高密度和高空间分辨率，但由于其固有的3D几何形状和庞大的体积，处理LiDAR数据具有挑战性。但是这样的高分辨率数据集在许多应用中具有巨大的潜力，并且在3D目标检测和识别中具有巨大的潜力。在这项研究中，我们提出了基于图神经网络（GNN）的框架来学习和识别三维激光雷达点云中的对象。GNN是一类深度学习，它基于图学习的原理学习模式和对象，在各种3D计算机视觉任务中取得了成功。
Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach
New Jersey Institute of Technology（新泽西理工学院）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Adaptive Graph Pruning with Sudden-Events Evaluation for Traffic Prediction using Online Semi-Decentralized ST-GNNs
University of Zagreb, Faculty of Electrical Engineering and Computing（Zagreb大学电子工程与计算学院）；RISE Research Institutes of Sweden（瑞典RISE研究机构）；KTH Royal Institute of Technology（皇家理工学院）
Geometric GNNs for Charged Particle Tracking at GlueX
Thomas Jefferson National Accelerator Facility（托马斯·杰斐逊国家加速器设施）；Department of Computer Science, University of Houston（休斯顿大学计算机科学系）；Department of Physics, William & Mary（威廉与玛丽学院物理系）；Computer Science Department, Old Dominion University（老 Dominion 大学计算机科学系）
Semi-decentralized Training of Spatio-Temporal Graph Neural Networks for Traffic Prediction
University of Zagreb, Faculty of Electrical Engineering and Computing（Zagreb大学电气工程与计算学院）；RISE Research Institutes of Sweden（瑞典RISE研究机构）；KTH Royal Institute of Technology（皇家理工学院）
Weisfeiler Leman for Euclidean Equivariant Machine Learning
Faculty of Mathematics, Technion - Israel Institute of Technology（以色列理工学院数学学院）；Faculty of Computer Science, Technion - Israel Institute of Technology（以色列理工学院计算机科学学院）
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
Texas A&M University（德州农工大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Georgia（佐治亚大学）；Rice University（莱斯大学）；Samsung Electronics America（三星电子美国公司）；Samsung Electronics（三星电子）
VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks
Program in Computing, Boise State University, Boise, Idaho, USA（博伊西州立大学计算项目）；Department of Mathematics, UCLA, Los Angeles, CA, USA（洛杉矶大学数学系）；Department of Computer Science, Yale University, New Haven, CT, USA（耶鲁大学计算机科学系）；Department of Genetics, Yale University, New Haven, CT, USA（耶鲁大学遗传学系）；Department of Mathematics, Boise State University, Boise, Idaho, USA（博伊西州立大学数学系）
ShanghaiTech University（上海科技大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging, School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院智能视觉与成像上海工程技术研究中心）
When Does Advection-Aware Graph Nowcasting Help? A Controlled Study of Distributed Solar Ramp Forecasting with a Self-Supervised Cloud-Motion Estimator
Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes
The Graduate Center, The City University of New York（纽约市立大学研究生中心）；Hunter College, The City University of New York（纽约市立大学亨特学院）；Weill Cornell Medicine, Cornell University（康奈尔大学威尔医学院）
PrivGNN: High-Performance Secure Inference for Cryptographic Graph Neural Networks
Deakin University（德肯大学）；Singapore University of Technology and Design（新加坡科技与设计大学）；Fuzhou University（福州市大学）；East China Normal University（华东师范大学）；Griffith University（格里菲斯大学）
Enhancing Steering Estimation with Semantic-Aware GNNs
Hybrid Intelligence part of Capgemini Engineering（凯捷工程混合智能部门）；Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）
Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction
Tsinghua University（清华大学）；University of Maryland, College Park（马里兰大学帕克分校）；Beijing National Research Center for Information Science and Technology (BNRist)（北京信息科学与技术国家研究中心（BNRist））
