We present a novel approach for metric dense depth estimation based on the fusion of a single-view image and a sparse, noisy Radar point cloud. The direct fusion of heterogeneous Radar and image data, or their encodings, tends to yield dense depth maps with significant artifacts, blurred boundaries, and suboptimal accuracy. To circumvent this issue, we learn to augment versatile and robust monocular depth prediction with the dense metric scale induced from sparse and noisy Radar data. We propose a Radar-Camera framework for highly accurate and fine-detailed dense depth estimation with four stages, including monocular depth prediction, global scale alignment of monocular depth with sparse Radar points, quasi-dense scale estimation through learning the association between Radar points and image patches, and local scale refinement of dense depth using a scale map learner. Our proposed method significantly outperforms the state-of-the-art Radar-Camera depth estimation methods by reducing the mean absolute error (MAE) of depth estimation by 25.6% and 40.2% on the challenging nuScenes dataset and our self-collected ZJU-4DRadarCam dataset, respectively. Our code and dataset will be released at \url{https://github.com/MMOCKING/RadarCam-Depth}.
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）
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）；School of Information and Electrical Engineering, Hangzhou City University（杭州城市学院信息与电气工程学院）；Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province（浙江省协同感知与自主无人机系统重点实验室）
Recently, transformer networks have outperformed traditional deep neural networks in natural language processing and show a large potential in many computer vision tasks compared to convolutional backbones. In the original transformer, readout tokens are used as designated vectors for aggregating information from other tokens. However, the performance of using readout tokens in a vision transformer is limited. Therefore, we propose a novel fusion strategy to integrate radar data into a dense prediction transformer network by reassembling camera representations with radar representations. Inste…
Radar-Based NLoS Pedestrian Localization for Darting-Out Scenarios Near Parked Vehicles with Camera-Assisted Point Cloud Interpretation
Seoul National University（首尔国立大学）；Samsung Electro-Mechanics Co., Ltd.（三星机电有限公司）；Chungnam National University（全南国立大学）；Ajou University（亚运会大学）；Institute of Engineering Research at Seoul National University（首尔国立大学工程研究所）
This paper addresses the problem of dense depth predictions from sparse distance sensor data and a single camera image on challenging weather conditions. This work explores the significance of different sensor modalities such as camera, Radar, and Lidar for estimating depth by applying Deep Learning approaches. Although Lidar has higher depth-sensing abilities than Radar and has been integrated with camera images in lots of previous works, depth estimation using CNN's on the fusion of robust Radar distance data and camera images has not been explored much. In this work, a deep regression netwo…
Michigan State University（密歇根州立大学）；Ford Motor Company（福特汽车公司）
A distinctive feature of Doppler radar is the measurement of velocity in the radial direction for radar points. However, the missing tangential velocity component hampers object velocity estimation as well as temporal integration of radar sweeps in dynamic scenes. Recognizing that fusing camera with radar provides complementary information to radar, in this paper we present a closed-form solution for the point-wise, full-velocity estimate of Doppler returns using the corresponding optical flow from camera images. Additionally, we address the association problem between radar returns and camera…
Multi-view radar-camera fused 3D object detection provides a farther detection range and more helpful features for autonomous driving, especially under adverse weather. The current radar-camera fusion methods deliver kinds of designs to fuse radar information with camera data. However, these fusion approaches usually adopt the straightforward concatenation operation between multi-modal features, which ignores the semantic alignment with radar features and sufficient correlations across modals. In this paper, we present MVFusion, a novel Multi-View radar-camera Fusion method to achieve semantic…
Cross-Dataset Experimental Study of Radar-Camera Fusion in Bird's-Eye View
Stellantis, Opel Automobile GmbH（斯泰兰蒂斯集团，欧宝汽车有限公司）；Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau（莱茵兰-普法尔茨凯泽斯劳滕-兰道工业大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
REOcc: Camera-Radar Fusion with Radar Feature Enrichment for 3D Occupancy Prediction
School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院电子工程学院）；Department of Automobile and IT Convergence, Kookmin University（韩国庆熙大学汽车与IT融合系）；Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院移动技术研究生院）；Robotics Program, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院机器人项目）
Michigan State University（密歇根州立大学）；Ford Motor Company（福特汽车公司）
While radar and video data can be readily fused at the detection level, fusing them at the pixel level is potentially more beneficial. This is also more challenging in part due to the sparsity of radar, but also because automotive radar beams are much wider than a typical pixel combined with a large baseline between camera and radar, which results in poor association between radar pixels and color pixel. A consequence is that depth completion methods designed for LiDAR and video fare poorly for radar and video. Here we propose a radar-to-pixel association stage which learns a mapping from rada…
University of Washington（华盛顿大学）；Zhejiang University（浙江大学）；Silkwave Holdings Limited（Silkwave控股有限公司）
Radar has long been a common sensor on autonomous vehicles for obstacle ranging and speed estimation. However, as a robust sensor to all-weather conditions, radar's capability has not been well-exploited, compared with camera or LiDAR. Instead of just serving as a supplementary sensor, radar's rich information hidden in the radio frequencies can potentially provide useful clues to achieve more complicated tasks, like object classification and detection. In this paper, we propose a new dataset, named CRUW, with a systematic annotator and performance evaluation system to address the radar object…
RC-BEVFusion: A Plug-In Module for Radar-Camera Bird's Eye View Feature Fusion
Stellantis, Opel Automobile GmbH（斯泰兰蒂斯集团欧宝汽车有限公司）；Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau（莱茵兰-普法尔茨凯泽斯劳滕-兰道工业大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
The University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）
In this paper we present a novel radar-camera sensor fusion framework for accurate object detection and distance estimation in autonomous driving scenarios. The proposed architecture uses a middle-fusion approach to fuse the radar point clouds and RGB images. Our radar object proposal network uses radar point clouds to generate 3D proposals from a set of 3D prior boxes. These proposals are mapped to the image and fed into a Radar Proposal Refinement (RPR) network for objectness score prediction and box refinement. The RPR network utilizes both radar information and image feature maps to genera…
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）
fortiss GmbH（福蒂斯有限公司）；Technical University of Munich（慕尼黑工业大学）
Most intelligent transportation systems use a combination of radar sensors and cameras for robust vehicle perception. The calibration of these heterogeneous sensor types in an automatic fashion during system operation is challenging due to differing physical measurement principles and the high sparsity of traffic radars. We propose - to the best of our knowledge - the first data-driven method for automatic rotational radar-camera calibration without dedicated calibration targets. Our approach is based on a coarse and a fine convolutional neural network. We employ a boosting-inspired training a…
Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey
School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；State Key Laboratory of Industrial Control Technology, Zhejiang University（浙江大学工业控制技术国家重点实验室）；Hangzhou Dianzi University（杭州电子科技大学）；Pillar of Information Systems Technology and Design, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计 pillar）
CLLAP: Contrastive Learning-based LiDAR-Augmented Pretraining for Enhanced Radar-Camera Fusion
Wuhan University of Technology（武汉理工大学）；University of North Carolina at Charlotte（北卡罗来纳州立大学）；Wuhan University（武汉大学）；Southwest Jiaotong University（西南交通大学）；The Hong Kong Polytechnic University（香港理工大学）
The perception system in autonomous vehicles is responsible for detecting and tracking the surrounding objects. This is usually done by taking advantage of several sensing modalities to increase robustness and accuracy, which makes sensor fusion a crucial part of the perception system. In this paper, we focus on the problem of radar and camera sensor fusion and propose a middle-fusion approach to exploit both radar and camera data for 3D object detection. Our approach, called CenterFusion, first uses a center point detection network to detect objects by identifying their center points on the i…
CLRNet: Targetless Extrinsic Calibration for Camera, Lidar and 4D Radar Using Deep Learning
Hungarian Research Network Institute for Computer Science and Control (HUN-REN SZTAKI)（匈牙利研究网络计算机科学与控制研究所）；Pázmány Péter Catholic University（帕兹曼·佩斯特天主教大学）；Intelligent Vehicles Group, TU Delft（智能车辆组，代尔夫特理工大学）；Perciv AI
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）
Environmental perception with the multi-modal fusion of radar and camera is crucial in autonomous driving to increase accuracy, completeness, and robustness. This paper focuses on utilizing millimeter-wave (MMW) radar and camera sensor fusion for 3D object detection. A novel method that realizes the feature-level fusion under the bird's-eye view (BEV) for a better feature representation is proposed. Firstly, radar points are augmented with temporal accumulation and sent to a spatial-temporal encoder for radar feature extraction. Meanwhile, multi-scale image 2D features which adapt to various s…
Object detection in camera images, using deep learning has been proven successfully in recent years. Rising detection rates and computationally efficient network structures are pushing this technique towards application in production vehicles. Nevertheless, the sensor quality of the camera is limited in severe weather conditions and through increased sensor noise in sparsely lit areas and at night. Our approach enhances current 2D object detection networks by fusing camera data and projected sparse radar data in the network layers. The proposed CameraRadarFusionNet (CRF-Net) automatically lear…
Télécom Paris, Institut Polytechnique de Paris（巴黎理工学院电信巴黎）
High quality perception is essential for autonomous driving (AD) systems. To reach the accuracy and robustness that are required by such systems, several types of sensors must be combined. Currently, mostly cameras and laser scanners (lidar) are deployed to build a representation of the world around the vehicle. While radar sensors have been used for a long time in the automotive industry, they are still under-used for AD despite their appealing characteristics (notably, their ability to measure the relative speed of obstacles and to operate even in adverse weather conditions). To a large exte…
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）
Simulating Automotive Radar with Lidar and Camera Inputs
Institute of Robotics and Automatic Information System, Nankai University（机器人与自动信息系统研究所，南开大学）；Tianjin Key Laboratory of Intelligent Robotics（天津智能机器人重点实验室）；TBI center, Nankai University（南开大学TBI中心）；Department of Robotics, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)（人工智能 Mohamed Bin Zayed 大学机器人系）
Radar Guided Camera Verification for Automatic Emergency Braking Rethinking Object Detection in Radar Camera Fusion
Autonomous Systems Lab, Department of Mechatronics Engineering, SRM Institute of Science and Technology（SRM科学与技术学院机电工程系自主系统实验室）；Centre for Electric Mobility, Department of EEE, SRM Institute of Science and Technology（SRM科学与技术学院电气与电子工程系电动出行中心）
DinoRADE: Full Spectral Radar-Camera Fusion with Vision Foundation Model Features for Multi-class Object Detection in Adverse Weather
Infineon Technologies AG, Austria（英飞凌科技公司，奥地利）；Graz University of Technology, Austria（格拉茨技术大学，奥地利）；Virtual Vehicle Research GmbH, Austria（虚拟车辆研究有限公司，奥地利）
LXLv2: Enhanced LiDAR Excluded Lean 3D Object Detection with Fusion of 4D Radar and Camera
School of Automation Science and Electrical Engineering, Beihang University（北京航空航天大学自动化科学与电气工程学院）；Continental Autonomous Mobility (Shanghai) Co., Ltd（大陆汽车自动驾驶（上海）有限公司）
