Recent studies have highlighted the promising application of NeRF in autonomous driving contexts. However, the complexity of outdoor environments, combined with the restricted viewpoints in driving scenarios, complicates the task of precisely reconstructing scene geometry. Such challenges often lead to diminished quality in reconstructions and extended durations for both training and rendering. To tackle these challenges, we present Lightning NeRF. It uses an efficient hybrid scene representation that effectively utilizes the geometry prior from LiDAR in autonomous driving scenarios. Lightning NeRF significantly improves the novel view synthesis performance of NeRF and reduces computational overheads. Through evaluations on real-world datasets, such as KITTI-360, Argoverse2, and our private dataset, we demonstrate that our approach not only exceeds the current state-of-the-art in novel view synthesis quality but also achieves a five-fold increase in training speed and a ten-fold improvement in rendering speed. Codes are available at https://github.com/VISION-SJTU/Lightning-NeRF .
DriveEnv-NeRF: Exploration of A NeRF-Based Autonomous Driving Environment for Real-World Performance Validation
NVIDIA AI Technology Center, NVIDIA Corporation（英伟达公司英伟达人工智能技术中心）；MediaTek Inc.（联发科股份有限公司）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Elsa Lab, National Tsing Hua University（国立清华大学埃尔莎实验室）
FreeDriveRF: Monocular RGB Dynamic NeRF without Poses for Autonomous Driving via Point-Level Dynamic-Static Decoupling
Department of Automation, Key Laboratory of System Control and Information Processing of Ministry of Education, Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education, Shanghai Engineering Research Center of Intelligent Control and Management（自动化系、教育部长系统控制与信息处理重点实验室、教育部长海洋智能设备与系统重点实验室、上海智能控制与管理工程研究中心）；Dimanshen Technology Co., Ltd.（迪马森科技有限公司）；Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, School of Information and Control Engineering, Advanced Robotics Research Center, China University of Mining and Technology（地下空间智能控制工程研究中心、教育部长、信息与控制工程学院、先进机器人研究中心、中国矿业大学）
MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving
AIR, Tsinghua University（清华大学AIR）；System Thurst, HKUST(GZ)（香港科技大学（广州）System Thurst）；HKUST（香港科技大学）；McGill University（麦吉尔大学）；Beijing Institute of Technology（北京理工大学）；National University of Singapore（新加坡国立大学）；HKU（香港大学）；University of Wisconsin Madison（威斯康星大学麦迪逊分校）；Xi’an University of Architecture and Technology（西安建筑科技大学）；Imperial College London（伦敦帝国学院）；Zhejiang University（浙江大学）
HybridOcc: NeRF Enhanced Transformer-based Multi-Camera 3D Occupancy Prediction
Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）；China FAW Group Corp., Ltd.（中国第一汽车集团有限公司）；Engineering Research Center of AI and Robotics, Ministry of Education（教育部人工智能与机器人工程研究中心）；Jilin Provincial Key Laboratory of Intelligence Science and Engineering（吉林省智能科学与工程重点实验室）
PreSight: Enhancing Autonomous Vehicle Perception with City-Scale NeRF Priors
IIIS, Tsinghua University（清华大学信息科学技术学院（简称：姚期智讲座教授信息技术科学学院））；University of Science and Technology Beijing（北京科技大学）；University of Southern California（南加州大学）；Shanghai Qi Zhi Institute（上海期智研究院）
Adv3D: Generating 3D Adversarial Examples for 3D Object Detection in Driving Scenarios with NeRF
Artificial Intelligence Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）人工智能学科）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程学系）
本文提出 Adv3D，首次将对抗样本建模为 NeRF，并通过基元感知采样与语义引导正则化生成可物理实现的 3D 伪装纹理补丁，攻击驾驶场景中的 3D 检测器。
UC-NeRF: Neural Radiance Field for Under-Calibrated Multi-view Cameras in Autonomous Driving
University of Science and Technology of China（中国科学技术大学）；PKU-Wuhan Institute for Artificial Intelligence（北大武汉人工智能研究院）；DJI Technology（大疆科技）；ShanghaiTech University（上海科技大学）
UAV See, UGV Do: Aerial Imagery and Virtual Teach Enabling Zero-Shot Ground Vehicle Repeat
University of Toronto Robotics Institute（多伦多大学机器人研究所）；University of Toronto Institute of Aerospace Studies（多伦多大学航空航天研究学院）；Ingenuity Labs（Ingenuity实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Neural Radiance Field in Autonomous Driving: A Survey
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与学院）；AI Thrust, Information Hub, The Hong Kong University of Science and Technology - Guangzhou Campus（香港科技大学广州校区信息枢纽AI中心）；School of Engineering Mathematics and Technology, University of Bristol（布里斯托大学工程数学与技术学院）
GaussianPretrain: A Simple Unified 3D Gaussian Representation for Visual Pre-training in Autonomous Driving
University of Macau（澳门大学）；Beijing Institute of Technology（北京理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Jiaotong University（北京交通大学）
RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision
National Key Laboratory for Multimedia Information Processing（多媒体信息处理国家重点实验室）；School of CS, Peking University（北京大学计算机学院）；Xiaomi Car（小米汽车）；Nanjing University（南京大学）；Nanyang Technological University（南洋理工大学）；CUHK MMLAB（香港中文大学多媒体实验室）
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features
NVIDIA Research（英伟达研究院）；University of Toronto（多伦多大学）；University of Southern California（南加州大学）；National University of Singapore（新加坡国立大学）；Stanford University（斯坦福大学）
