We propose a novel point cloud U-Net diffusion architecture for 3D generative modeling capable of generating high-quality and diverse 3D shapes while maintaining fast generation times. Our network employs a dual-branch architecture, combining the high-resolution representations of points with the computational efficiency of sparse voxels. Our fastest variant outperforms all non-diffusion generative approaches on unconditional shape generation, the most popular benchmark for evaluating point cloud generative models, while our largest model achieves state-of-the-art results among diffusion methods, with a runtime approximately 70% of the previously state-of-the-art PVD. Beyond unconditional generation, we perform extensive evaluations, including conditional generation on all categories of ShapeNet, demonstrating the scalability of our model to larger datasets, and implicit generation which allows our network to produce high quality point clouds on fewer timesteps, further decreasing the generation time. Finally, we evaluate the architecture's performance in point cloud completion and super-resolution. Our model excels in all tasks, establishing it as a state-of-the-art diffusion U-Net for point cloud generative modeling. The code is publicly available at https://github.com/JohnRomanelis/SPVD.git.
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（电子与计算机工程系，香港科学与技术大学）；Microelectronics Thrust, The Hong Kong University of Science and Technology (GZ)（微电子领域，香港科学与技术大学（GZ））
PVNet: Point-Voxel Interaction LiDAR Scene Upsampling Via Diffusion Models
Telecom Guizhou Branch（贵州电信支局）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；College of Engineering, Huaqiao University（华侨大学工程学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology（哈尔滨工业大学深圳研究生院生物计算研究中心）；Key Laboratory of Network Oriented Intelligent Computation（网络导向智能计算重点实验室）
GeoSVR: Taming Sparse Voxels for Geometrically Accurate Surface Reconstruction
School of Computer Science and Engineering, State Key Laboratory of Complex Critical Software Environment, Jiangxi Research Institute, Beihang University（北京航空航天大学计算机科学与工程学院、复杂临界软件环境国家重点实验室、江西研究院）；Rawmantic AI；State Key Laboratory of Virtual Reality Technology and Systems, Beijing（虚拟现实技术与系统国家重点实验室）；Macquarie University（麦考瑞大学）；RIKEN AIP（日本理化学研究所AIP）；The University of Tokyo（东京大学）
Surfsvr: 2D Surface Priors as 3D Geometric Regularizers for Sparse Voxel Reconstruction
HIT (Shenzhen)（哈尔滨工业大学（深圳））；Tsinghua University（清华大学）；NWPU（西北工业大学）；Beijing Institute of Control and Engineering（北京控制与工程研究所）；Technical University of Munich（慕尼黑工业大学）
SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Department of Management Science and Engineering, Tsinghua University（清华大学管理科学与工程系）
MsSVT++: Mixed-scale Sparse Voxel Transformer with Center Voting for 3D Object Detection
Beijing Institute of Technology（北京理工大学）；Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China（光电成像技术与系统教育部重点实验室）；Beijing Institute of Technology Chongqing Innovation Center（北京理工大学重庆创新中心）
Structure-Adaptive Sparse Diffusion in Voxel Space for 3D Medical Image Enhancement
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA（佛罗里达大学电气与计算机工程系）；Department of Medicine, University of Florida, Gainesville, FL, USA（佛罗里达大学医学系）；Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA（佛罗里达大学生物医学工程系）；Research Computing, University of Florida, Gainesville, FL, USA（佛罗里达大学研究计算中心）；NVIDIA, Santa Clara, CA, USA（英伟达）
自动驾驶汽车必须快速识别驾驶环境以确保安全驾驶。随着激光雷达（Light Detection And Range，LiDAR）传感器在自动驾驶汽车中的广泛应用，对传感器帧速率范围内的点云进行逐点分类的快速语义分割在驾驶环境识别中引起了人们的关注。基于体素和融合的语义分割模型是目前点云语义分割的主流模型，但由于体素分辨率高，计算量大，影响了其实时性。本文提出了一种基于点卷积和三维稀疏卷积的快速体素语义分割模型（PCSCNet）。所提出的模型的设计优于在高和低体素分辨率使用点卷积为基础的特征提取。此外，该模型在特征提取后使用3D稀疏卷积加速特征传播。实验结果表明，该模型在语义分割方面优于SemanticKITTI和nuScenes的实时模型，在LiDAR点云推理方面达到了实时性要求。
FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels
Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所模式识别国家重点实验室智能感知与计算研究中心）；TuSimple（图森未来）
FSHNet: Fully Sparse Hybrid Network for 3D Object Detection
School of Computer Science and Engineering, Sun Yat-sen University（计算机科学与工程学院，中山大学）；Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University（机器智能与高级计算关键实验室，中山大学）；School of Artificial Intelligence, Sun Yat-sen University（人工智能学院，中山大学）
