自动驾驶

SB-BEVFusion: Enhancing the Robustness against Sensor Malfunction and Corruptions

SB-BEVFusion：增强对传感器故障和损坏的鲁棒性

Markus Essl, Marta Moscati, Mubashir Noman, Muhammad Zaigham Zaheer, Usman Naseem, Shah Nawaz, Markus Schedl

机构 * Johannes Kepler University Linz（约翰·凯撒大学林茨分校） ； MBZUAI（穆罕默德·本·拉希德人工智能研究所） ； Macquarie University（麦考瑞大学） ； Linz Institute of Technology（林茨技术学院） 纠错

专题命中 激光雷达 ：BEV(abstract,abstract_cn)；LiDAR(abstract)；分类 cs.CV

AI总结 本文提出SB-BEVFusion框架，通过处理缺失或损坏的相机和激光雷达数据，提升自动驾驶3D目标检测在传感器故障和损坏场景下的鲁棒性。

Comments Accepted at ICIP 2026

Adaptive Dual-Weighted Gravitational Point Cloud Denoising Method

自适应双权重引力点云去噪方法

Ge Zhang, Chunyang Wang, Bin Liu, Guan Xi

专题命中 激光雷达 ：autonomous driving(abstract)；LiDAR(abstract)；occupancy(abstract)；分类 cs.CV

AI总结 本文提出一种自适应双权重引力点云去噪方法，通过八叉树空间划分、自适应体素统计和引力评分函数，提升去噪精度、边缘保持和实时性能。

Panoptic-CUDAL: Rural Australia Point Cloud Dataset in Rainy Conditions

Tzu-Yun Tseng, Alexey Nekrasov, Malcolm Burdorf, Bastian Leibe, Julie Stephany Berrio, Mao Shan, Zhenxing Ming, Stewart Worrall

机构 * The Australian Centre for Robotics (ACFR) at the University of Sydney（澳大利亚机器人中心（ACFR）于悉尼大学） ； RWTH Aachen University（亚琛RWTH大学） 纠错

专题命中 激光雷达 ：autonomous driving(abstract)；LiDAR(abstract)；occupancy(abstract)；分类 cs.CV

Diffusion-Based mmWave Radar Point Cloud Enhancement Driven by Range Images

Ruixin Wu, Zihan Li, Jin Wang, Xiangyu Xu, Zhi Zheng, Kaixiang Huang, Guodong Lu

机构 * The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University（浙江大学机械工程学院流体动力与机械电子系统国家重点实验室） ； Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems, Zhejiang University（浙江大学工业大数据与机器人智能系统重点实验室） ； Robotics Research Center of Yuyao City, Ningbo（宁波余姚市机器人研究中心） ； State Key Laboratory of Robotics and Systems, Department of Mechatronics Engineering, Harbin Institute of Technology（哈尔滨工业大学机器人系统国家重点实验室） 纠错

专题命中 激光雷达 ：autonomous driving(abstract)；BEV(abstract)；LiDAR(abstract)；分类 cs.RO

Comments 8 pages, 7 figures. This work has been submitted to the IEEE for possible publication

PanoNet3D: Combining Semantic and Geometric Understanding for LiDARPoint Cloud Detection

Xia Chen, Jianren Wang, David Held, Martial Hebert

专题命中 激光雷达 ：autonomous driving(abstract)；LiDAR(abstract)；driving perception(abstract)；分类 cs.CV

Comments 3DV2020

Through the Perspective of LiDAR: A Feature-Enriched and Uncertainty-Aware Annotation Pipeline for Terrestrial Point Cloud Segmentation

通过LiDAR视角：一种特征丰富且不确定性感知的标注流程用于陆地点云分割

Fei Zhang, Rob Chancia, Josie Clapp, Amirhossein Hassanzadeh, Dimah Dera, Richard MacKenzie, Jan van Aardt

机构 * Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA（切斯特·F·卡尔森成像科学中心，罗切斯特理工学院，罗切斯特，纽约州，美国） 纠错

专题命中 激光雷达 ：LiDAR(title)；分类 cs.RO、cs.CV

AI总结 本文提出一种半自动标注流程，通过球面投影和特征丰富技术提升陆地点云分割的效率与精度，构建了Mangrove3D数据集并验证了特征重要性，为生态监测提供高质量分割方案。

Comments 40 pages (28 main text), 20 figures, 4 supplementary materials; links to 3D point animations are included in the last table

Zero-shot detection of buildings in mobile LiDAR using Language Vision Model

June Moh Goo, Zichao Zeng, Jan Boehm

机构 * Department of Civil, Environmental and Geomatic Engineering, University College London（伦敦大学学院建筑、环境与地理工程系） 纠错

专题命中 激光雷达 ：LiDAR(title)；分类 cs.CV、cs.AI

Comments 7 pages, 6 figures, conference

Journal ref The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2024)

Comparing roughness maps generated by five roughness descriptors for LiDAR-derived digital elevation models

Lei Fan, Yang Zhao

专题命中 激光雷达 ：LiDAR(title)；分类 cs.CV、eess.IV

Comments 14 pages, 6 figures

DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors

Xiaze Zhang, Ziheng Ding, Qi Jing, Yuejie Zhang, Wenchao Ding, Rui Feng

专题命中 激光雷达 ：LiDAR(title)；分类 cs.RO、cs.CV

A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing

Kangcheng Liu

专题命中 激光雷达 ：LiDAR(title)；分类 cs.RO、cs.CV

Comments International Conference on Robotics and Automation (ICRA) 2022, 6 Figures. arXiv admin note: substantial text overlap with arXiv:2312.01262

Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition

Milad Ramezani, Liang Wang, Joshua Knights, Zhibin Li, Pauline Pounds, Peyman Moghadam

专题命中 激光雷达 ：LiDAR(title)；分类 cs.RO、cs.CV

Comments 10 pages, 5 figures, 5 tables, Accepted for Publication in the IEEE Robotics and Automation Letters (RA-L)

HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion

Yu Wang, Shuhui Bu, Lin Chen, Yifei Dong, Kun Li, Xuefeng Cao, Ke Li

专题命中 激光雷达 ：LiDAR(title)；分类 cs.RO、cs.CV

P2Net: A Post-Processing Network for Refining Semantic Segmentation of LiDAR Point Cloud based on Consistency of Consecutive Frames

Yutaka Momma, Weimin Wang, Edgar Simo-Serra, Satoshi Iizuka, Ryosuke Nakamura, Hiroshi Ishikawa

专题命中 激光雷达 ：LiDAR(title)；分类 cs.RO、cs.CV

Comments 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups

Jorge Beltrán, Carlos Guindel, Arturo de la Escalera, Fernando García

专题命中 激光雷达 ：LiDAR(title)；分类 cs.RO、cs.CV

Comments Published on IEEE Transactions on Intelligent Transportation Systems

Journal ref IEEE Transactions on Intelligent Transportation Systems, 2022

Translation Invariant Global Estimation of Heading Angle Using Sinogram of LiDAR Point Cloud

Xiaqing Ding, Xuecheng Xu, Sha Lu, Yanmei Jiao, Mengwen Tan, Rong Xiong, Huanjun Deng, Mingyang Li, Yue Wang

专题命中 激光雷达 ：LiDAR(title)；分类 cs.RO、cs.CV

Comments Paper accepted in the 2022 IEEE International Conference on Robotics and Automation (ICRA)

Robot Localization and Navigation through Predictive Processing using LiDAR

Daniel Burghardt, Pablo Lanillos

专题命中 激光雷达 ：LiDAR(title)；分类 cs.RO、cs.AI

Comments 2nd International Workshop on Active Inference IWAI2021, European Conference on Machine Learning (ECML/PCKDD 2021)

Person-MinkUNet: 3D Person Detection with LiDAR Point Cloud

Dan Jia, Bastian Leibe

专题命中 激光雷达 ：LiDAR(title)；分类 cs.RO、cs.CV

Comments accepted as an extended abstract in JRDB-ACT Workshop at CVPR21

FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling

Kangcheng Liu, Zhi Gao, Feng Lin, Ben M. Chen

专题命中 激光雷达 ：LiDAR(title)；分类 cs.RO、cs.CV

3D Surface Reconstruction from Voxel-based Lidar Data

Luis Roldão, Raoul de Charette, Anne Verroust-Blondet

专题命中 激光雷达 ：LiDAR(title)；分类 cs.RO、cs.CV

Comments IEEE Intelligent Transportation Systems Conference (ITSC) 2019

WiWorld-RealData: A Real-World Multi-Modal Dataset for 6G Wireless World Models

WiWorld-RealData：用于6G无线世界模型的真实世界多模态数据集

Yinyin Jiao, Huixin Xu, Jianhua Zhang, Yuelong Qiu, Jingjing Wang, Shaoyi Liu, Yuxiang Zhang, Li Yu, Xuebin Sun, Guangyi Liu

专题命中 激光雷达 ：LiDAR(summary_cn,abstract_cn)

AI总结 提出WiWorld-RealData数据集，包含3.7 GHz和6.775 GHz的信道冲激响应、多视角图像、全景图像、LiDAR点云、毫米波雷达记录和GNSS轨迹，支持环境辅助的信道预测，路径损耗预测MAE为2.02 dB。

Comments 6 pages, 6 figures, 3 tables

Single-Frame Point-Pixel Registration via Supervised Cross-Modal Feature Matching

基于监督跨模态特征匹配的单帧点像素配准

Yu Han, Zhiwei Huang, Yanting Zhang, Fangjun Ding, Shen Cai, Xiaoyu Tang, Yanchao Dong, Rui Fan

机构 * School of Information and Intelligent Science, Donghua University（信息与智能科学学院，东华大学） 纠错

专题命中 激光雷达 ：autonomous driving(abstract)；LiDAR(abstract)；分类 cs.RO、cs.CV、cs.AI

AI总结 研究激光雷达点云和相机图像的点像素配准难题，提出基于投影的无检测器框架及重复性评分机制，通过实验证明该方法在单帧激光雷达下能达先进性能，优于现有方法。

IntentNet: Learning to Predict Intention from Raw Sensor Data

Sergio Casas, Wenjie Luo, Raquel Urtasun

专题命中 激光雷达 ：self-driving(abstract)；LiDAR(abstract)；分类 cs.RO、cs.CV、cs.AI

Comments CoRL 2018

A Quick Review on Recent Trends in 3D Point Cloud Data Compression Techniques and the Challenges of Direct Processing in 3D Compressed Domain

Mohammed Javed, MD Meraz, Pavan Chakraborty

专题命中 激光雷达 ：autonomous driving(abstract)；LiDAR(abstract)；分类 cs.RO、cs.CV、eess.IV

SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances

Yancheng Pan, Biao Gao, Jilin Mei, Sibo Geng, Chengkun Li, Huijing Zhao

专题命中 激光雷达 ：autonomous driving(abstract)；LiDAR(abstract)；分类 cs.RO、cs.CV、eess.IV

Comments submited to IEEE Intelligent Vehicles Symposium(2020)

NormalView: tree species classification from backpack and aerial lidar data using geometric projections

NormalView：利用几何投影从背包和航空激光雷达数据进行树种分类

Juho Korkeala, Jesse Muhojoki, Josef Taher, Klaara Salolahti, Matti Hyyppä, Antero Kukko, Juha Hyyppä

机构 * Finnish Geospatial Research Institute FGI（芬兰地理研究 institute FGI） ； The National Land Survey of Finland（芬兰国家土地调查局） ； Department of Remote Sensing and Photogrammetry（遥感与摄影测量系） ； Department of Built Environment, School of Engineering（环境系，工程学院） 纠错

专题命中 激光雷达 ：LiDAR(title)；分类 cs.CV

AI总结 研究利用NormalView方法，通过将几何信息嵌入二维投影并输入YOLOv11网络进行树种分类，在移动和航空激光雷达数据上测试，发现多传感器强度信息有益，该方法仅依赖几何信息，公开了相关数据集，取得较好分类结果。

Comments 25 pages, 10+4 figures

TreeLoc++: Robust 6-DoF LiDAR Localization in Forests with a Compact Digital Forest Inventory

TreeLoc++：利用紧凑数字森林清单在森林中进行稳健的6自由度激光雷达定位

Minwoo Jung, Dongjae Lee, Nived Chebrolu, Haedam Oh, Maurice Fallon, Ayoung Kim

机构 * Department of Mechanical Engineering, Seoul National University, Seoul, South Korea（首尔国立大学机械工程系，韩国首尔） ； Department of Computer Science and Engineering, Indian Institute of Technology Bombay, India（印度班加罗尔印度理工学院计算机科学与工程系） ； Oxford Robotics Institute, Department of Engineering Science, University of Oxford, Oxford, UK（牛津大学奥克斯福德机器人研究所，英国牛津） 纠错

专题命中 激光雷达 ：LiDAR(title)；分类 cs.RO

AI总结 研究提出TreeLoc++框架，直接基于数字森林清单定位，以紧凑几何属性编码树干。通过距离直方图等减少误匹配，优化估计姿态，不依赖密集点云数据，实现厘米级精度定位，具长期部署扩展性。

Comments 30 pages, 33 figures and 15 tables

Lidar Scan Registration Robust to Extreme Motions

对极端运动鲁棒的激光雷达扫描配准

Simon-Pierre Deschênes, Dominic Baril, Vladimír Kubelka, Philippe Giguère, François Pomerleau

机构 * Northern Robotics Laboratory（北方机器人实验室） 纠错

专题命中 激光雷达 ：LiDAR(title)；分类 cs.RO

AI总结 针对极端运动下点云畸变导致配准失败的问题，提出一种考虑轨迹运动不确定性和环境几何的去畸变方法，在200 m/s^2和800 rad/s^2的峰值加速度下，平移误差降低9.26%，旋转误差降低21.84%。

Comments 8 pages, 8 figures, published in 2021 18th Conference on Robots and Vision (CRV), Burnaby, Canada

Journal ref 2021 18th Conference on Robots and Vision (CRV), 2021, pp. 17-24

APCoTTA: Continual Test-Time Adaptation for Semantic Segmentation of Airborne LiDAR Point Clouds

APCoTTA: 用于空中激光雷达点云语义分割的连续测试时间适应

Yuan Gao, Shaobo Xia, Sheng Nie, Cheng Wang, Xiaohuan Xi, Bisheng Yang

机构 * Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空航天信息研究所） ； International Research Center of Big Data for Sustainable Development Goals（可持续发展目标大数据国际研究中心） ； University of Chinese Academy of Sciences（中国科学院大学） ； The Department of Geomatics Engineering, Changsha University of Science and Technology（长沙理工大学测绘工程系） ； The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室） 纠错

专题命中 激光雷达 ：LiDAR(title)；分类 cs.CV

AI总结 本文提出APCoTTA框架，针对空中激光雷达点云语义分割中的持续领域偏移问题，通过梯度驱动层选择、熵一致性损失和随机参数插值机制提升适应性能，并构建两个基准数据集。

Comments 18 pages,12 figures

Journal ref ISPRS Journal of Photogrammetry and Remote Sensing Volume 237, July 2026, Pages 339-354

FLaTEC: Frequency-Disentangled Latent Triplanes for Efficient Compression of LiDAR Point Clouds

FLaTEC：频率解耦的潜在三平面用于激光雷达点云高效压缩

Xiaoge Zhang, Zijie Wu, Mingtao Feng, Zichen Geng, Mehwish Nasim, Saeed Anwar, Ajmal Mian

机构 * University of Western Australia（西澳大学） ； Xidian University（西安电子科技大学） 纠错

专题命中 激光雷达 ：LiDAR(title)；分类 cs.CV

AI总结 FLaTEC通过频率解耦的潜在三平面方法，实现激光雷达点云的高效压缩，达到最佳率失真性能，并在多个数据集上超越传统编码器。

Unsupervised deep learning for semantic segmentation of multispectral LiDAR forest point clouds

Lassi Ruoppa, Oona Oinonen, Josef Taher, Matti Lehtomäki, Narges Takhtkeshha, Antero Kukko, Harri Kaartinen, Juha Hyyppä

机构 * Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, The National Land Survey of Finland（遥感与摄影测量系，芬兰地理研究 institute FGI，芬兰国家土地测绘局） ； D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK)（3D光学计量（3DOM）单元，布鲁诺·凯瑟尔基金会（FBK）） ； Department of Built Environment, Aalto University, School of Engineering（环境学院，艾尔托大学，工程学院） 纠错

专题命中 激光雷达 ：LiDAR(title)；分类 cs.CV

Comments 30 pages, 10 figures

Journal ref ISPRS Journal of Photogrammetry and Remote Sensing, Volume 228, 2025, pages 694-722