8 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…
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…
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…