SD4R: Sparse-to-Dense Learning for 3D Object Detection with 4D Radar
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 Global Innovation Center, Zhejiang University（浙江大学宁波全球创新中心）；School of Automotive Studies, Tongji University（同济大学汽车学院）
4D radar measurements offer an affordable and weather-robust solution for 3D perception. However, the inherent sparsity and noise of radar point clouds present significant challenges for accurate 3D object detection, underscoring the need for effective and robust point clouds densification. Despite recent progress, existing densification methods often fail to address the extreme sparsity of 4D radar point clouds and exhibit limited robustness when processing scenes with a small number of points. In this paper, we propose SD4R, a novel framework that transforms sparse radar point clouds into dense representations. SD4R begins by utilizing a foreground point generator (FPG) to mitigate noise propagation and produce densified point clouds. Subsequently, a logit-query encoder (LQE) enhances conventional pillarization, resulting in robust feature representations. Through these innovations, our SD4R demonstrates strong capability in both noise reduction and foreground point densification. Extensive experiments conducted on the publicly available View-of-Delft dataset demonstrate that SD4R achieves state-of-the-art performance. Source code is available at https://github.com/lancelot0805/SD4R.
Sparse4D-Radar: An Efficient and Robust Framework for Surround-View 3D Object Detection via 4D Radar-Camera Fusion
State Key Laboratory of Ocean Sensing and Ocean College, Zhejiang University（浙江大学海洋传感与海洋学院海洋传感技术国家重点实验室）；Engineering Research Center of Oceanic Sensing Technology and Equipment, Ministry of Education, Zhejiang University（浙江大学海洋传感技术与装备教育部工程研究中心）；Donghai Laboratory（东海实验室）；Ocean College, Institute of Marine Electronics and Intelligent Systems, Zhejiang University（浙江大学海洋学院海洋电子与智能系统研究所）
SemRaFiner: Panoptic Segmentation in Sparse and Noisy Radar Point Clouds
CARIAD SE；University of Bonn（波恩大学）；Technical University of Munich（慕尼黑工业大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）
Enhanced 3D Object Detection via Diverse Feature Representations of 4D Radar Tensor
Graduate School of Advanced Security Science and Technology, Korea Advanced Institute of Science and Technology（韩国科学技术院高级安全科学与技术研究生院）；CCS Graduate School of Mobility, Korea Advanced Institute of Science and Technology（韩国科学技术院移动技术研究生院）；Centre Inria d’Univeristé Côte d’Azur（尼斯大学Inria中心）
Boosting Instance Awareness via Cross-View Correlation with 4D Radar and Camera for 3D Object Detection
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；School of Automotive Studies, Tongji University（同济大学汽车学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Jinhua Institute of Zhejiang University（浙江大学金华研究院）；School of Information and Electrical Engineering, Hangzhou City University（杭州城市学院信息与电气工程学院）；Hangzhou City University Binjiang Innovation Center（杭州城市学院滨江创新中心）
RCTrans: Radar-Camera Transformer via Radar Densifier and Sequential Decoder for 3D Object Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院）
Improving Greenland Bed Topography Mapping with Uncertainty-Aware Graph Learning on Sparse Radar Data
iHARP, University of Maryland Baltimore County (UMBC)（iHARP，马里兰大学巴尔的摩县分校）；Department of Information Systems, University of Maryland Baltimore County (UMBC)（信息系统系，马里兰大学巴尔的摩县分校）
Learning Subglacial Bed Topography from Sparse Radar with Physics-Guided Residuals
iHARP, University of Maryland Baltimore County (UMBC)（iHARP，马里兰大学巴尔的摩县分校）；Department of Information Systems, University of Maryland Baltimore County (UMBC)（信息系统系，马里兰大学巴尔的摩县分校）
RC-GeoCP: Geometric Consensus for 4D Radar-Camera Collaborative Perception
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；School of Automotive Studies, Tongji University（同济大学汽车学院）；Thrust of Artificial Intelligence, Hong Kong University of Science and Technology（香港科技大学人工智能研究所）
RadarXFormer: Robust Object Detection via Cross-Dimension Fusion of 4D Radar Spectra and Images for Autonomous Driving
Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学未来技术全球研究院）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能感知学院）；Key Laboratory of System Control and Information Processing, Ministry of Education of China（中国教育部系统控制与信息处理重点实验室）；SAIC GM Wuling Automobile Company Co., Ltd.（上汽通用五菱汽车有限公司）；Guangxi Laboratory of New Energy Automobile（广西新能源汽车实验室）
RaCalNet: Radar Calibration Network for Sparse-Supervised Metric Depth Estimation
School of Automation and Intelligent Sensing, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China（上海交通大学自动化与智能感知学院，医学机器人研究所）；School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China（上海交通大学数学科学学院）；South China University of Technology, Guangzhou, China（华南理工大学）；State Grid Intelligence Technology CO., LTD（国家电网智能技术有限公司）
TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion
S-Lab, Nanyang Technological University（新加坡南洋理工大学S-Lab）；School of AIA, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；SenseTime Research（商汤科技研究院）
Self-Supervised Moving Object Segmentation of Sparse and Noisy Radar Point Clouds
CARIAD SE（CARIAD公司）；TU Dortmund University（杜伊斯堡-艾森大学）；University of Bonn（波恩大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔人工智能与机器学习研究所）
Towards Dense and Accurate Radar Perception Via Efficient Cross-Modal Diffusion Model
Huzhou Institute, Zhejiang University（浙江大学湖州研究院）；Nanyang Technological University（南洋理工大学）；School of Cyber Science and Technology, University of Science and Technology of China（中国科学技术大学网络空间安全学院）；Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院 cyber系统与控制研究所）
CRAB: Camera-Radar Fusion for Reducing Depth Ambiguity in Backward Projection based View Transformation
Interdisciplinary Program in Artificial intelligence, Seoul National University（人工智能跨学科项目，首尔国立大学）；Cho Chun Shik Graduate School of Mobility, KAIST（移动性研究生院，韩国科学技术院）；dot Inc（42dot公司）；Electronics and Telecommunications Research Institute (ETRI)（电子电信研究院）；Department of Automobile and IT Convergence, Kookmin University（汽车与IT融合系，韩国釜山大学）
RCBEVDet++: Toward High-accuracy Radar-Camera Fusion 3D Perception Network
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）
ZFusion: An Effective Fuser of Camera and 4D Radar for 3D Object Perception in Autonomous Driving
School of Data Science, Fudan University（复旦大学数据科学学院）；ZF (China) Investment Co., Ltd.（采埃孚（中国）投资有限公司）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Registering the 4D Millimeter Wave Radar Point Clouds Via Generalized Method of Moments
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能感知学院）；Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究院）；Department of Statistics, Uppsala University（乌普萨拉大学统计系）
OG-PCL: Efficient Sparse Point Cloud Processing for Human Activity Recognition
Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing, 211189, China（教育部水下声学信号处理重点实验室，东南大学，南京）
Segment Any Motion with Radar: Robust Multimodal Moving-Object Segmentation and Tracking
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Great Bay University, Dongguan（大湾区大学（东莞））；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
