自动驾驶 - arXivDaily 专题

2508.01778 2025-08-05 cs.CV cs.RO 88%

DiffSemanticFusion: Semantic Raster BEV Fusion for Autonomous Driving via Online HD Map Diffusion

Zhigang Sun, Yiru Wang, Anqing Jiang, Shuo Wang, Yu Gao, Yuwen Heng, Shouyi Zhang, An He, Hao Jiang, Jinhao Chai, Zichong Gu, Wang Jijun, Shichen Tang, Lavdim Halilaj, Juergen Luettin, Hao Sun

机构 * Bosch Corporate Research, Bosch (China) Investment Ltd.（博世企业研究院、博世（中国）投资有限公司）； School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）； Shanghai Jiaotong University（上海交通大学）； AIR, Tsinghua University（清华大学人工智能研究院）； Robert Bosch GmbH（罗伯特·博世集团）

专题命中 BEV与占用：autonomous driving(title,abstract)；BEV(title,abstract)；分类 cs.RO、cs.CV

2401.01065 2024-06-19 cs.CV cs.AI 88%

BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving

Tao Tang, Dafeng Wei, Zhengyu Jia, Tian Gao, Changwei Cai, Chengkai Hou, Peng Jia, Kun Zhan, Haiyang Sun, Jingchen Fan, Yixing Zhao, Fu Liu, Xiaodan Liang, Xianpeng Lang, Yang Wang

专题命中 BEV与占用：autonomous driving(title,abstract)；BEV(title,abstract)；分类 cs.CV、cs.AI

2405.20337 2024-05-31 cs.CV cs.AI 88%

OccSora: 4D Occupancy Generation Models as World Simulators for Autonomous Driving

Lening Wang, Wenzhao Zheng, Yilong Ren, Han Jiang, Zhiyong Cui, Haiyang Yu, Jiwen Lu

专题命中 BEV与占用：autonomous driving(title,abstract)；occupancy(title,abstract)；分类 cs.CV、cs.AI

Comments Code is available at: https://github.com/wzzheng/OccSora

2306.15349 2023-06-28 cs.CV cs.AI 88%

SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion

Jianbiao Mei, Yu Yang, Mengmeng Wang, Tianxin Huang, Xuemeng Yang, Yong Liu

专题命中 BEV与占用：BEV(title,abstract)；LiDAR(title)；autonomous driving(abstract)；分类 cs.CV、cs.AI

Comments 8 pages, 5 figures, IROS2023

2304.00525 2023-04-04 cs.CV cs.RO 88%

One Training for Multiple Deployments: Polar-based Adaptive BEV Perception for Autonomous Driving

Huitong Yang, Xuyang Bai, Xinge Zhu, Yuexin Ma

专题命中 BEV与占用：autonomous driving(title,abstract)；BEV(title,abstract)；分类 cs.RO、cs.CV

Comments 8 pages,4 figures. Accepted by IEEE International Conference on Robotics and Automation (ICRA)

2404.14027 2024-06-13 cs.CV cs.LG 88%

OccFeat: Self-supervised Occupancy Feature Prediction for Pretraining BEV Segmentation Networks

Sophia Sirko-Galouchenko, Alexandre Boulch, Spyros Gidaris, Andrei Bursuc, Antonin Vobecky, Patrick Pérez, Renaud Marlet

专题命中 BEV与占用：BEV(title,abstract)；occupancy(title,abstract)；分类 cs.CV；autonomous driving(comments)

Comments Accepted to CVPR 2024, Workshop on Autonomous Driving

2312.12144 2023-12-20 cs.CV 88%

M-BEV: Masked BEV Perception for Robust Autonomous Driving

Siran Chen, Yue Ma, Yu Qiao, Yali Wang

专题命中 BEV与占用：BEV(title,abstract)；autonomous driving(title,abstract)；分类 cs.CV

Comments Github repository: https://github.com/Sranc3/M-BEV

2405.14014 2024-10-29 cs.CV cs.AI cs.LG cs.RO 88%

RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar

Fangqiang Ding, Xiangyu Wen, Yunzhou Zhu, Yiming Li, Chris Xiaoxuan Lu

专题命中 BEV与占用：occupancy(title,abstract)；autonomous driving(abstract)；self-driving(abstract)；LiDAR(abstract)

Comments 22 pages, 5 figures, 8 tables. Accepted by NeurIPS 2024 (Vancouver), the Thirty-Eighth Annual Conference on Neural Information Processing Systems

2406.08691 2024-06-14 cs.CV cs.AI cs.LG cs.RO 88%

UnO: Unsupervised Occupancy Fields for Perception and Forecasting

Ben Agro, Quinlan Sykora, Sergio Casas, Thomas Gilles, Raquel Urtasun

专题命中 BEV与占用：occupancy(title,abstract)；self-driving(abstract)；BEV(abstract)；LiDAR(abstract)

2603.27294 2026-03-31 cs.CV 88%

Class-Distribution Guided Active Learning for 3D Occupancy Prediction in Autonomous Driving

基于类分布的主动学习用于自动驾驶中的3D占用预测

Wonjune Kim, In-Jae Lee, Sihwan Hwang, Sanmin Kim, Dongsuk Kum

机构 * KAIST（韩国科学技术院）； Seoul National University（首尔大学）； Kookmin University（国民大学）

专题命中 BEV与占用：autonomous driving(title,abstract)；occupancy(title,abstract)；分类 cs.CV

AI总结本文提出一种基于类分布的主动学习框架，通过三种互补标准选择训练样本，以解决自动驾驶中3D占用预测的类别不平衡和标注成本问题，实现在仅42.4%标注数据下达到26.62 mIoU的性能。

Comments IEEE RA-L 2026

Journal ref IEEE Robotics and Automation Letters (2026)

2603.27238 2026-03-31 cs.CV 88%

An Instance-Centric Panoptic Occupancy Prediction Benchmark for Autonomous Driving

面向自动驾驶的实例中心全景占用预测基准测试

Yi Feng, Junwu E, Zizhan Guo, Yu Ma, Hanli Wang, Rui Fan

机构 * Tongji University（同济大学）

专题命中 BEV与占用：autonomous driving(title,abstract)；occupancy(title,abstract)；分类 cs.CV

AI总结本文提出实例中心的全景占用预测基准测试，引入ADMesh和CarlaOcc数据集，提供高精度3D模型和实例级标注，建立标准化评估指标并系统评估代表性模型。

Comments Accepted to CVPR 2026. Code and dataset are available at https://mias.group/CarlaOcc

2506.17004 2026-01-19 cs.CV 88%

A Synthetic Benchmark for Collaborative 3D Semantic Occupancy Prediction in V2X-Enabled Autonomous Driving

用于V2X赋能自动驾驶的协作3D语义占用预测合成基准

Hanlin Wu, Pengfei Lin, Ehsan Javanmardi, Naren Bao, Bo Qian, Hao Si, Manabu Tsukada

机构 * The School of Information Science and Technology, The University of Tokyo（信息科学与技术学系，东京大学）； National Institute of Informatics（信息机构）

专题命中 BEV与占用：autonomous driving(title,abstract)；occupancy(title,abstract)；分类 cs.CV

AI总结本文提出用于V2X赋能自动驾驶的协作3D语义占用预测合成基准，通过高分辨率传感器和基线模型提升预测性能。

2511.15396 2025-11-20 cs.CV 88%

ShelfOcc: Native 3D Supervision beyond LiDAR for Vision-Based Occupancy Estimation

ShelfOcc: 无需激光雷达的基于视觉的占用估计的原生3D监督

Simon Boeder, Fabian Gigengack, Simon Roesler, Holger Caesar, Benjamin Risse

机构 * Bosch Research（博世研究院）； University of Münster（穆尼黑大学）； TU Delft（代尔夫特理工大学）

专题命中 BEV与占用：LiDAR(title,abstract)；occupancy(title,abstract)；分类 cs.CV

AI总结 ShelfOcc提出一种无需激光雷达的视觉方法，通过生成一致的3D语义体素标签实现高质量监督，显著提升无激光雷达3D场景理解性能。

2509.09110 2025-09-12 cs.CV 88%

S-BEVLoc: BEV-based Self-supervised Framework for Large-scale LiDAR Global Localization

Chenghao Zhang, Lun Luo, Si-Yuan Cao, Xiaokai Bai, Yuncheng Jin, Zhu Yu, Beinan Yu, Yisen Wang, Hui-Liang Shen

机构 * College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）； Ningbo Innovation Center, Zhejiang University（浙江大学宁波创新中心）； College of Information Engineering, China Jiliang University（中国浙江师范大学信息工程学院）； Jinhua Institute of Zhejiang University（浙江大学金华研究院）

专题命中 BEV与占用：BEV(title,abstract)；LiDAR(title,abstract)；分类 cs.CV

Journal ref in IEEE Robotics and Automation Letters, vol. 10, no. 10, pp. 9614-9621, Oct. 2025

2505.23115 2025-07-04 cs.CV 88%

Diffusion-Based Generative Models for 3D Occupancy Prediction in Autonomous Driving

Yunshen Wang, Yicheng Liu, Tianyuan Yuan, Yingshi Liang, Xiuyu Yang, Honggang Zhang, Hang Zhao

机构 * Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）； Beijing University of Posts and Telecommunications（北京邮电大学）

专题命中 BEV与占用：autonomous driving(title,abstract)；occupancy(title,abstract)；分类 cs.CV

Comments ICRA 2025

2507.00707 2025-07-02 cs.CV 88%

BEV-VAE: Multi-view Image Generation with Spatial Consistency for Autonomous Driving

Zeming Chen, Hang Zhao

机构 * Shanghai Qi Zhi Institute（上海启智研究所）； IIIS, Tsinghua University（清华大学智能工程研究所）

专题命中 BEV与占用：autonomous driving(title,abstract)；BEV(title,abstract)；分类 cs.CV

2406.07042 2025-04-09 cs.CV 88%

EFFOcc: Learning Efficient Occupancy Networks from Minimal Labels for Autonomous Driving

Yining Shi, Kun Jiang, Jinyu Miao, Ke Wang, Kangan Qian, Yunlong Wang, Jiusi Li, Tuopu Wen, Mengmeng Yang, Yiliang Xu, Diange Yang

专题命中 BEV与占用：autonomous driving(title,abstract)；occupancy(title,abstract)；分类 cs.CV

Comments Code at https://github.com/synsin0/EFFOcc

2502.07309 2025-02-12 cs.CV 88%

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving

Xiang Li, Pengfei Li, Yupeng Zheng, Wei Sun, Yan Wang, Yilun Chen

专题命中 BEV与占用：autonomous driving(title,abstract)；occupancy(title,abstract)；分类 cs.CV

Comments Accepted by ICLR 2025

2502.06419 2025-02-11 cs.RO 88%

Occ-LLM: Enhancing Autonomous Driving with Occupancy-Based Large Language Models

Tianshuo Xu, Hao Lu, Xu Yan, Yingjie Cai, Bingbing Liu, Yingcong Chen

专题命中 BEV与占用：autonomous driving(title,abstract)；occupancy(title,abstract)；分类 cs.RO

Comments Accepted in 2025 IEEE International Conference on Robotics and Automation (ICRA)

2412.14821 2024-12-20 cs.CV 88%

PC-BEV: An Efficient Polar-Cartesian BEV Fusion Framework for LiDAR Semantic Segmentation

Shoumeng Qiu, Xinrun Li, XiangYang Xue, Jian Pu

专题命中 BEV与占用：BEV(title,abstract)；LiDAR(title,abstract)；分类 cs.CV

Comments AAAI 2025

2407.12491 2024-07-29 cs.CV 88%

Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving

Yuqi Dai, Jian Sun, Shengbo Eben Li, Qing Xu, Jianqiang Wang, Lei He, Keqiang Li

专题命中 BEV与占用：autonomous driving(title,abstract)；BEV(title,abstract)；分类 cs.CV

2405.02595 2024-07-09 cs.CV 88%

Vision-based 3D occupancy prediction in autonomous driving: a review and outlook

Yanan Zhang, Jinqing Zhang, Zengran Wang, Junhao Xu, Di Huang

专题命中 BEV与占用：autonomous driving(title,abstract)；occupancy(title,abstract)；分类 cs.CV

Comments 20 pages, 20 figures

2407.02192 2024-07-03 cs.RO 88%

Categorized Grid and Unknown Space Causes for LiDAR-based Dynamic Occupancy Grids

Víctor Jiménez-Bermejo, Jorge Godoy, Antonio Artuñedo, Jorge Villagra

专题命中 BEV与占用：LiDAR(title,abstract)；occupancy(title,abstract)；分类 cs.RO

Comments Submitted to the 27th IEEE International Conference on Intelligent Transportation Systems

2404.15014 2024-04-24 cs.CV 88%

OccGen: Generative Multi-modal 3D Occupancy Prediction for Autonomous Driving

Guoqing Wang, Zhongdao Wang, Pin Tang, Jilai Zheng, Xiangxuan Ren, Bailan Feng, Chao Ma

专题命中 BEV与占用：occupancy(title,abstract)；autonomous driving(title)；LiDAR(abstract)；分类 cs.CV

2403.02280 2024-03-05 cs.RO 88%

Tightly-Coupled LiDAR-Visual-Inertial SLAM and Large-Scale Volumetric Occupancy Mapping

Simon Boche, Sebastián Barbas Laina, Stefan Leutenegger

专题命中 BEV与占用：LiDAR(title,abstract)；occupancy(title,abstract)；分类 cs.RO

Comments IEEE International Conference on Robotics and Automation (ICRA) 2024

2305.16829 2024-01-12 cs.CV 88%

BEV-IO: Enhancing Bird's-Eye-View 3D Detection with Instance Occupancy

Zaibin Zhang, Yuanhang Zhang, Lijun Wang, Yifan Wang, Huchuan Lu

专题命中 BEV与占用：BEV(title,abstract)；occupancy(title,abstract)；分类 cs.CV

Comments v2

2307.08493 2023-10-06 cs.RO 88%

Occupancy Grid Mapping without Ray-Casting for High-resolution LiDAR Sensors

Yixi Cai, Fanze Kong, Yunfan Ren, Fangcheng Zhu, Jiarong Lin, Fu Zhang

专题命中 BEV与占用：LiDAR(title,abstract)；occupancy(title,abstract)；分类 cs.RO

Comments Supplementary material included. Accepted for publication in IEEE Transactions on Robotics (T-RO)

2308.05443 2023-08-11 cs.RO 88%

Occupancy Grid Map to Pose Graph-based Map: Robust BIM-based 2D-LiDAR Localization for Lifelong Indoor Navigation in Changing and Dynamic Environments

Miguel Arturo Vega Torres, Alexander Braun, André Borrmann

专题命中 BEV与占用：LiDAR(title,abstract)；occupancy(title,abstract)；分类 cs.RO

Comments 8 pages, 5 figures, conference: eWork and eBusiness in Architecture, Engineering and Construction: ECPPM 2022. booktitle: eWork and eBusiness in Architecture, Engineering and Construction: ECPPM 2022,editors: Eilif Hjelseth, Sujesh F. Sujan, and Raimar Scherer, publisher: CRC Press, year: 2022,month: Sep,isbn: 978-1-003-35422-2

2203.03875 2022-03-09 cs.RO cs.LG 88%

Occupancy Flow Fields for Motion Forecasting in Autonomous Driving

Reza Mahjourian, Jinkyu Kim, Yuning Chai, Mingxing Tan, Ben Sapp, Dragomir Anguelov

专题命中 BEV与占用：autonomous driving(title,abstract)；occupancy(title,abstract)；分类 cs.RO

Journal ref IEEE Robotics and Automation Letters

2112.04282 2022-01-19 cs.RO cs.LG 88%

Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities

Pou-Chun Kung, Chieh-Chih Wang, Wen-Chieh Lin

专题命中 BEV与占用：LiDAR(title,abstract)；occupancy(title)；autonomous driving(abstract)；分类 cs.RO

Comments Accepted for publication in RA-L 2022

1. BEV与占用 3760 篇

DiffSemanticFusion: Semantic Raster BEV Fusion for Autonomous Driving via Online HD Map Diffusion

BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving

OccSora: 4D Occupancy Generation Models as World Simulators for Autonomous Driving

SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion

One Training for Multiple Deployments: Polar-based Adaptive BEV Perception for Autonomous Driving

OccFeat: Self-supervised Occupancy Feature Prediction for Pretraining BEV Segmentation Networks

M-BEV: Masked BEV Perception for Robust Autonomous Driving

RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar

UnO: Unsupervised Occupancy Fields for Perception and Forecasting

Class-Distribution Guided Active Learning for 3D Occupancy Prediction in Autonomous Driving

An Instance-Centric Panoptic Occupancy Prediction Benchmark for Autonomous Driving

A Synthetic Benchmark for Collaborative 3D Semantic Occupancy Prediction in V2X-Enabled Autonomous Driving

ShelfOcc: Native 3D Supervision beyond LiDAR for Vision-Based Occupancy Estimation

S-BEVLoc: BEV-based Self-supervised Framework for Large-scale LiDAR Global Localization

Diffusion-Based Generative Models for 3D Occupancy Prediction in Autonomous Driving

BEV-VAE: Multi-view Image Generation with Spatial Consistency for Autonomous Driving

EFFOcc: Learning Efficient Occupancy Networks from Minimal Labels for Autonomous Driving

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving

Occ-LLM: Enhancing Autonomous Driving with Occupancy-Based Large Language Models

PC-BEV: An Efficient Polar-Cartesian BEV Fusion Framework for LiDAR Semantic Segmentation

Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving

Vision-based 3D occupancy prediction in autonomous driving: a review and outlook

Categorized Grid and Unknown Space Causes for LiDAR-based Dynamic Occupancy Grids

OccGen: Generative Multi-modal 3D Occupancy Prediction for Autonomous Driving

Tightly-Coupled LiDAR-Visual-Inertial SLAM and Large-Scale Volumetric Occupancy Mapping

BEV-IO: Enhancing Bird's-Eye-View 3D Detection with Instance Occupancy

Occupancy Grid Mapping without Ray-Casting for High-resolution LiDAR Sensors

Occupancy Grid Map to Pose Graph-based Map: Robust BIM-based 2D-LiDAR Localization for Lifelong Indoor Navigation in Changing and Dynamic Environments

Occupancy Flow Fields for Motion Forecasting in Autonomous Driving

Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities