Along with increasingly popular virtual reality applications, the three-dimensional (3D) point cloud has become a fundamental data structure to characterize 3D objects and surroundings. To process 3D point clouds efficiently, a suitable model for the underlying structure and outlier noises is always critical. In this work, we propose a hypergraph-based new point cloud model that is amenable to efficient analysis and processing. We introduce tensor-based methods to estimate hypergraph spectrum components and frequency coefficients of point clouds in both ideal and noisy settings. We establish an analytical connection between hypergraph frequencies and structural features. We further evaluate the efficacy of hypergraph spectrum estimation in two common point cloud applications of sampling and denoising for which also we elaborate specific hypergraph filter design and spectral properties. The empirical performance demonstrates the strength of hypergraph signal processing as a tool in 3D point clouds and the underlying properties.
Geometric-Aware Hypergraph Reasoning for Novel Class Discovery in Point Cloud Segmentation
School of Artificial Intelligence, College of Intelligence and Computing, Tianjin University（人工智能学院、智能计算学院、天津大学）；School of Computer Science and Information Engineering, Hefei University of Technology（计算机科学与信息工程学院、合肥工业大学）；Department of Computer Science City St George’s, University of London（伦敦大学城市圣乔治学院计算机科学系）
Deep learning for 3D point cloud processing -- from approaches, tasks to its implications on urban and environmental applications
Capital Normal University（首都师范大学）；China University of Mining and Technology (Beijing)（中国矿业大学（北京））；University of British Columbia（不列颠哥伦比亚大学）；The Ohio State University（俄亥俄州立大学）
Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation
Peoples’ Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（RUDN大学））；Federal Research Center ”Computer Science and Control” of Russian Academy of Sciences（俄罗斯科学院计算机科学与控制联邦研究中心）；National Research University Higher School of Economics（国立研究型大学高等经济学院）
PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling
The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；South China University of Technology（华南理工大学）；Tencent AI Lab（腾讯人工智能实验室）
Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis
Fudan University（复旦大学）；University of Science and Technology of China（中国科学技术大学）；Shanghai AI Laboratory（上海人工智能实验室）；University of Shanghai for Science and Technology（上海理工大学）
Differentiable Convolution Search for Point Cloud Processing
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Huawei Cloud & AI（华为云与人工智能）；Xidian University（西安电子科技大学）；Centre for Artificial Intelligence and Robotics, HK Institute of Science & Innovation, CAS（中国科学院香港创新研究院人工智能与机器人创新中心）
Optimizing Sparse Convolution on GPUs with CUDA for 3D Point Cloud Processing in Embedded Systems
Brion Technologies, an ASML Company（Brion Technologies（ASML旗下公司））；School of Mechanical Engineering and Automation, Northeastern University（东北大学机械工程与自动化学院）
该研究针对嵌入式系统中的 3D 点云处理，指出点云稀疏且无规则网格所带来的计算挑战，并聚焦于利用 CUDA 优化 GPU 上的稀疏卷积。
DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Department of Automation, Tsinghua University（清华大学自动化系）
3D Point Cloud Processing and Learning for Autonomous Driving
Mitsubishi Electric Research Laboratories（三菱电机研究实验室）；Precivision Technologies, Inc.（Precivision科技公司）；New York University（纽约大学）；Uber Advanced Technologies Group（优步先进技术集团）
Scalable Hypergraph Structure Learning with Diverse Smoothness Priors
Department of Electrical and Computer Engineering, University of Kentucky（电气与计算机工程系，肯塔基大学）；Department of Electrical and Computer Engineering, University of Delaware（电气与计算机工程系，特拉华大学）
CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis
School of Computer and Information Engineering, Fujian Engineering Research Center for Medical Data Mining and Application, Xiamen University of Technology（厦门理工学院计算机与信息工程学院、福建省医学数据挖掘与应用工程研究中心）；Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University（厦门大学电子科学系、福建省等离子体与磁共振重点实验室）；Key Laboratory for Chemical Biology of Fujian Province, MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, College of Chemistry and Chemical Engineering, Xiamen University（厦门大学化学化工学院、福建省化学生物学重点实验室、教育部光谱分析与仪器重点实验室）；Department of Applied Marine Physics and Engineering, College of Ocean and Earth Sciences, Xiamen University（厦门大学海洋与地球学院应用海洋物理与工程系）；China Mobile Group（中国移动集团）
