6 papers
A Conditional U-Net Pipeline with Pre- and Post-Processing for Aerial RGB-to-Thermal Image Translation
Tseten Sherpa, Sikandar Ali, Shubham Parab +5
Paired RGB-thermal data has shown significant utility across a range of applications, including image fusion, object tracking, and anomaly detection; however, its broader adoption…
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…