15 citations · 16 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…