15 citations · 15 across the 15 of their papers we have counts for
15 papers
XD-RCDepth: Lightweight Radar-Camera Depth Estimation with Explainability-Aligned and Distribution-Aware Distillation
Huawei Sun, Zixu Wang, Xiangyuan Peng +4
Depth estimation remains central to autonomous driving, and radar-camera fusion offers robustness in adverse conditions by providing complementary geometric cues. In this paper, we…
Feature Identification for Hierarchical Contrastive Learning
Julius Ott, Nastassia Vysotskaya, Huawei Sun +2
Hierarchical classification is a crucial task in many applications, where objects are organized into multiple levels of categories. However, conventional classification approaches…
TRIDE: A Text-assisted Radar-Image weather-aware fusion network for Depth Estimation
Huawei Sun, Zixu Wang, Hao Feng +3
Depth estimation, essential for autonomous driving, seeks to interpret the 3D environment surrounding vehicles. The development of radar sensors, known for their cost-efficiency an…
ELMAR: Enhancing LiDAR Detection with 4D Radar Motion Awareness and Cross-modal Uncertainty
Xiangyuan Peng, Miao Tang, Huawei Sun +3
LiDAR and 4D radar are widely used in autonomous driving and robotics. While LiDAR provides rich spatial information, 4D radar offers velocity measurement and remains robust under…
CaRaFFusion: Improving 2D Semantic Segmentation with Camera-Radar Point Cloud Fusion and Zero-Shot Image Inpainting
Huawei Sun, Bora Kunter Sahin, Georg Stettinger +3
Segmenting objects in an environment is a crucial task for autonomous driving and robotics, as it enables a better understanding of the surroundings of each agent. Although camera…
4D mmWave Radar for Sensing Enhancement in Adverse Environments: Advances and Challenges
Xiangyuan Peng, Miao Tang, Huawei Sun +3
Intelligent transportation systems require accurate and reliable sensing. However, adverse environments, such as rain, snow, and fog, can significantly degrade the performance of L…
MutualForce: Mutual-Aware Enhancement for 4D Radar-LiDAR 3D Object Detection
Xiangyuan Peng, Huawei Sun, Kay Bierzynski +3
Radar and LiDAR have been widely used in autonomous driving as LiDAR provides rich structure information, and radar demonstrates high robustness under adverse weather. Recent studi…
LiRCDepth: Lightweight Radar-Camera Depth Estimation via Knowledge Distillation and Uncertainty Guidance
Huawei Sun, Nastassia Vysotskaya, Tobias Sukianto +5
Recently, radar-camera fusion algorithms have gained significant attention as radar sensors provide geometric information that complements the limitations of cameras. However, most…
GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsampling
Huawei Sun, Zixu Wang, Hao Feng +3
Depth estimation plays a pivotal role in autonomous driving, facilitating a comprehensive understanding of the vehicle's 3D surroundings. Radar, with its robustness to adverse weat…
CaFNet: A Confidence-Driven Framework for Radar Camera Depth Estimation
Huawei Sun, Hao Feng, Julius Ott +2
Depth estimation is critical in autonomous driving for interpreting 3D scenes accurately. Recently, radar-camera depth estimation has become of sufficient interest due to the robus…
MUFASA: Multi-View Fusion and Adaptation Network with Spatial Awareness for Radar Object Detection
Xiangyuan Peng, Miao Tang, Huawei Sun +3
In recent years, approaches based on radar object detection have made significant progress in autonomous driving systems due to their robustness under adverse weather compared to L…
Enhanced Radar Perception via Multi-Task Learning: Towards Refined Data for Sensor Fusion Applications
Huawei Sun, Hao Feng, Gianfranco Mauro +4
Radar and camera fusion yields robustness in perception tasks by leveraging the strength of both sensors. The typical extracted radar point cloud is 2D without height information d…
Multi-Task Cross-Modality Attention-Fusion for 2D Object Detection
Huawei Sun, Hao Feng, Georg Stettinger +2
Accurate and robust object detection is critical for autonomous driving. Image-based detectors face difficulties caused by low visibility in adverse weather conditions. Thus, radar…
Utilizing Explainable AI for improving the Performance of Neural Networks
Huawei Sun, Lorenzo Servadei, Hao Feng +3
Nowadays, deep neural networks are widely used in a variety of fields that have a direct impact on society. Although those models typically show outstanding performance, they have…
Label-Aware Ranked Loss for robust People Counting using Automotive in-cabin Radar
Lorenzo Servadei, Huawei Sun, Julius Ott +6
In this paper, we introduce the Label-Aware Ranked loss, a novel metric loss function. Compared to the state-of-the-art Deep Metric Learning losses, this function takes advantage o…