In this paper, we propose an end-to-end learning network to predict future frames in a point cloud sequence. As main novelty, an initial layer learns topological information of point clouds as geometric features, to form representative spatio-temporal neighborhoods. This module is followed by multiple Graph-RNN cells. Each cell learns points dynamics (i.e., RNN states) by processing each point jointly with the spatio-temporal neighbouring points. We tested the network performance with a MINST dataset of moving digits, a synthetic human bodies motions and JPEG dynamic bodies datasets. Simulation results demonstrate that our method outperforms baseline ones that neglect geometry features information.
3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds
Beijing Normal University（北京师范大学）；Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences（中国科学院地理科学与资源研究所）；Tsinghua University（清华大学）
Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds
University of California, Los Angeles（加州大学洛杉矶分校）；Shanghai Jiao Tong University（上海交通大学）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）；Peking University（北京大学）；Tsinghua University（清华大学）
MRIo3DS-Net: A Mutually Reinforcing Images to 3D Surface RNN-like framework for model-adaptation indoor 3D reconstruction
Central China Normal University（华中师范大学）；Hainan Surveying and Mapping Product Quality Supervision and Inspection Station, MNR（自然资源部海南测绘产品质量监督检验站）；Wuhan University（武汉大学）
Recurrent Slice Networks for 3D Segmentation of Point Clouds
University of Southern California（南加州大学）
点云是3D数据的有效数据格式。然而，现有的点云3D分割方法要么不对局部依赖\cite{pointnet}建模，要么需要增加计算\cite{kd-net,pointnet2}。这项工作提出了一种新的三维分割框架，RSNet \footnote{Codes are released here https://github.com/qianguih/RSNet}，有效地模拟点云的局部结构。RSNet的关键组件是一个轻量级的本地依赖模块。它是一个新的切片池层，递归神经网络（RNN）层和切片解池层的组合。切片池层被设计为将无序点的特征投影到有序的特征向量序列上，以便可以应用传统的端到端学习算法（RNN）。通过在S3DIS \cite{stanford}、ScanNet \cite{scannet}和ShapeNet \cite{shapenet}数据集上的综合实验，验证了RSNet的性能。在其最简单的形式中，RSNets超越了这些基准上所有以前的最先进的方法。并与现有的最先进的方法\cite{pointnet, pointnet2}进行了比较，证明了RSNets的有效性。
DUGAE: Unified Geometry and Attribute Enhancement via Spatiotemporal Correlations for G-PCC Compressed Dynamic Point Clouds
School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；Key Laboratory of Machine Intelligence and System Control, Ministry of Education（教育部机器智能与系统控制重点实验室）；Department of Computing and Decision Science, Lingnan University（岭南大学计算与决策科学系）
E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning
Intel Labs（英特尔实验室）；Apple Inc.（苹果公司）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, XMU（厦门大学多媒体可信感知与高效计算重点实验室）；Yancheng Institute Of Technology（盐城工学院）
UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling
State Key Laboratory of General Artificial Intelligence, Peking University, Shenzhen Graduate School（一般人工智能国家重点实验室，北京大学深圳研究生院）；The Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University（浙江大学-伊利诺伊大学 Urbana-Champaign 院，浙江大学）；State University of New York at Buffalo（纽约州立大学布法罗分校）
Learning Parallel Dense Correspondence from Spatio-Temporal Descriptors for Efficient and Robust 4D Reconstruction
South China University of Technology（华南理工大学）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；HKUST（香港科技大学）；Pazhou Lab（琶洲实验室）；Peng Cheng Laboratory（鹏城实验室）；The Hong Kong Polytechnic University（香港理工大学）
UST-Hand: An Uncertainty-aware Spatiotemporal Point Cloud Interaction Network for 3D Self-supervised Hand Pose Estimation
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；Beijing University of Posts and Telecommunications（北京邮电大学）；Sun Yat-sen University（中山大学）；Peking University（北京大学）；Defense Innovation Institute, Academy of Military Sciences（国防科技创新院，军事科学学院）；Tianjin Artificial Intelligence Innovation Center（天津人工智能创新中心）
GATS: Gaussian Aware Temporal Scaling Transformer for Invariant 4D Spatio-Temporal Point Cloud Representation
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家重点实验室）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究院）；Xi’an Jiaotong University（西安交通大学）；Harbin Institute of Technology（哈尔滨工业大学）；Pengcheng Laboratory（鹏城实验室）
Spatiotemporal Co-attention Recurrent Neural Networks for Human-Skeleton Motion Prediction
Nanjing University of Science and Technology（南京理工大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Huawei Cloud（华为云）；Tencent AI Lab（腾讯人工智能实验室）
Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures
Department of Medical Physics and Biomedical Engineering, University College London（伦敦大学学院医学物理与生物医学工程系）；Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London（伦敦大学学院韦尔科姆/工程与物理科学研究委员会介入与外科科学中心（WEISS））；School of Biomedical Engineering & Imaging Sciences (BMEIS), King’s College London（伦敦国王学院生物医学工程与成像科学学院（BMEIS））；UCL Queen Square Institute of Neurology, Department of Clinical and Experimental Epilepsy, London（伦敦大学学院女王广场神经学研究所临床与实验癫痫系）；UCL Queen Square Institute of Neurology, Department of Clinical and Experimental（伦敦大学学院女王广场神经学研究所临床与实验（此处原文不完整，按现有内容提取））
STS-Mixer: Spatio-Temporal-Spectral Mixer for 4D Point Cloud Video Understanding
Nanyang Technological University（南洋理工大学）；Alibaba Group（阿里巴巴集团）；Zhejiang University of Technology（浙江工业大学）；University of Chinese Academy of Sciences（中国科学院大学）
Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds
Beijing Institute of Technology（北京理工大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Indiana University（印第安纳大学）；University of Kentucky（肯塔基大学）
Spatio-Temporal Bi-directional Cross-frame Memory for Distractor Filtering Point Cloud Single Object Tracking
School of Electronic and Information Engineering, Changchun University of Science and Technology（长春理工大学电子信息工程学院）；Hong Kong Applied Science and Technology Research Institute（香港应用科技研究院）
LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention
Beijing Institute of Technology（北京理工大学）；Baidu Research（百度研究院）；Inception Institute of Artificial Intelligence（人工智能起源研究所）；National Engineering Laboratory of Deep Learning Technology and Application（深度学习技术及应用国家工程实验室）；University of Kentucky（肯塔基大学）
