LiDAR sensors provide rich 3D information about their surrounding{s} and are becoming increasingly important for autonomous vehicles tasks such as {localization}, semantic segmentation , object detection , and tracking. {Simulation} accelerates the testing, validation, and deployment of autonomous vehicles while {also} reducing cost and eliminating the risks of testing in real-world scenarios. We address the problem of high-fidelity LiDAR simulation and present a pipeline that leverages real-world point clouds acquired by mobile mapping systems. Point-based geometry representations, more specifically splats {(2D oriented disks with normals)}, have proven their ability to accurately model the underlying surface in large point clouds{, mainly with uniform density}. We introduce an adaptive splat generation method that accurately models the underlying 3D geometry {to handle real-world point clouds with variable densities}, especially for thin structures. Moreover, we introduce a {fast} LiDAR {sensor} simulator, {working} in the splatted model, {that leverages} the GPU parallel architecture with an acceleration structure while focusing on efficiently handling large point clouds. We test our LiDAR simulation in real-world conditions, showing qualitative and quantitative results compared to basic splatting and meshing techniques, demonstrating the interest of our modeling technique.
英文摘要：
LiDAR sensors provide rich 3D information about their surrounding{s} and are becoming increasingly important for autonomous vehicles tasks such as {localization}, semantic segmentation, object detection, and tracking. {Simulation} accelerates the testing, validation, and deployment of autonomous vehicles while {also} reducing cost and eliminating the risks of testing in real-world scenarios. We address the problem of high-fidelity LiDAR simulation and present a pipeline that leverages real-world point clouds acquired by mobile mapping systems. Point-based geometry representations, more specifically splats {(2D oriented disks with normals)}, have proven their ability to accurately model the underlying surface in large point clouds{, mainly with uniform density}. We introduce an adaptive splat generation method that accurately models the underlying 3D geometry {to handle real-world point clouds with variable densities}, especially for thin structures. Moreover, we introduce a {fast} LiDAR {sensor} simulator, {working} in the splatted model, {that leverages} the GPU parallel architecture with an acceleration structure while focusing on efficiently handling large point clouds. We test our LiDAR simulation in real-world conditions, showing qualitative and quantitative results compared to basic splatting and meshing techniques, demonstrating the interest of our modeling technique.
Transforming Omnidirectional RGB-LiDAR data into 3D Gaussian Splatting
Department of Computer Science, State University of New York（计算机科学系，纽约州立大学）；Department of Mechanical Engineering, State University of New York（机械工程系，纽约州立大学）
GS-Planner: A Gaussian-Splatting-based Planning Framework for Active High-Fidelity Reconstruction
Institute of Cyber-Systems and Control（cyber系统与控制研究所）；College of Control Science and Engineering（控制科学与工程学院）；Zhejiang University（浙江大学）；Huzhou Institute, Zhejiang University（浙江大学湖州研究院）
FGGS-LiDAR: Ultra-Fast, GPU-Accelerated Simulation from General 3DGS Models to LiDAR
Tsinghua University, Shenzhen, China（清华大学深圳研究院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；DISCOVER Robotics；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院（AIR））
LIT-GS: LiDAR-Inertial-Thermal Gaussian Splatting for Illumination-Robust Mapping
College of Mechatronics and Control Engineering, Shenzhen University（深圳大学机电与控制工程学院）；Department of Mechanical Engineering, The University of Hong Kong（香港大学机械工程系）
Wuhan University（武汉大学）；School of Computer Science（计算机学院）；School of Electronic Information（电子信息学院）；School of Artificial Intelligence（人工智能学院）；National Engineering Research Center for Multimedia Software（多媒体软件国家工程研究中心）；Institute for Math & AI（数学与人工智能研究所）
LI-GS: Gaussian Splatting with LiDAR Incorporated for Accurate Large-Scale Reconstruction
Zhejiang University（浙江大学）；College of Control Science and Engineering（控制科学与工程学院）；State Key Laboratory of Industrial Control Technology（工业控制技术国家重点实验室）；Key Laboratory of Collaborative sensing and autonomous unmanned systems of Zhejiang Province（浙江省协同感知与自主无人系统重点实验室）
Gaussian-LIC2: LiDAR-Inertial-Camera Gaussian Splatting SLAM
Institute of Cyber-Systems and Control（控制系统研究所）；Zhejiang University（浙江大学）；Department of Robotics（机器人系）；Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)（迈罗德·本·扎耶德人工智能大学）
AgriGS-SLAM: Orchard Mapping Across Seasons via Multi-View Gaussian Splatting SLAM
Dipartimento di Bioingegneria, Elettronica e Informazione, Politecnico di Milano（生物工程、电子与信息系，米兰理工大学）；Agricultural Biosystems Engineering, Wageningen University & Research（农业生物系统工程，瓦赫宁根大学与研究）
RMGS-SLAM: Real-time Multi-sensor Gaussian Splatting SLAM
Advanced Robotics Centre, National University of Singapore（新加坡国立大学先进机器人中心）；School of Aeronautics and Astronautics, Shanghai Jiao Tong University（上海交通大学航空宇航学院）；College of Computer Science, Sichuan University（四川大学计算机学院）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工联合研究技术联盟）
High-Fidelity Simulated Data Generation for Real-World Zero-Shot Robotic Manipulation Learning with Gaussian Splatting
Wuhan University（武汉大学）；DAMO Academy, Alibaba Group（达摩院，阿里巴巴集团）；Hupan Lab（虎扑实验室）；The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）；Huazhong University of Science and Technology（华中科技大学）；Zhejiang University（浙江大学）
CubifyGS: Object-Centric 3D Gaussian Splatting for Lifelong Dynamic Scene Maintenance
School of Automation, Beijing Institute of Technology, Beijing, China（北京理工大学自动化学院，北京，中国）；Guangzhou Saite Intelligent Technology Co., Ltd.（广州赛泰智能科技有限公司）
ActiveSplat: High-Fidelity Scene Reconstruction through Active Gaussian Splatting
School of Optics and Photonics, Beijing Institute of Technology（光学与光子学学院，北京理工大学）；Institute for AI Industry Research (AIR), Tsinghua University（人工智能产业研究院（AIR），清华大学）；School of Vehicle and Mobility, Tsinghua University（车辆与移动学院，清华大学）
MCGS-SLAM: A Multi-Camera SLAM Framework Using Gaussian Splatting for High-Fidelity Mapping
Department of Mathematics, ETH Zurich（苏黎世联邦理工学院数学系）；Department of Mechanical and Process Engineering, ETH Zurich（苏黎世联邦理工学院机械与工艺工程系）；Department of Informatics, University of Zurich（苏黎世大学信息系）；Institute for Photogrammetry, University of Stuttgart（斯图加特大学摄影测量研究所）；Computer Vision and Geometry Group, ETH Zurich（苏黎世联邦理工学院计算机视觉与几何组）；Microsoft Spatial AI Lab（微软空间AI实验室）；Computer Vision Research Group, University of Amsterdam（阿姆斯特丹大学计算机视觉研究组）
FGS-SLAM: Fourier-based Gaussian Splatting for Real-time SLAM with Sparse and Dense Map Fusion
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所机器人重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Mechanical and Energy Engineering, Southern University of Science and Technology（南方科技大学机械与能源工程系）
