2024/09/02 by Huawei Sun, Zixu Wang, Sun, Huawei +9 · 2 citations
Earth and Planetary Sciences · Engineering · Environmental Science · #3D Surveying and Cultural Heritage #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Remote Sensing and LiDAR Applications #Robotics and Sensor-Based Localization #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2409.02720
openalex publication_date 2024/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 weather conditions and capability to measure distances, has drawn significant interest for radar-camera depth estimation. However, existing algorithms process the inherently noisy and sparse radar data by projecting 3D points onto the image plane for pixel-level feature extraction, overlooking the valuable geometric information contained within the radar point cloud. To address this gap, we propose GET-UP, leveraging attention-enhanced Graph Neural Networks (GNN) to exchange and aggregate both 2D and 3D information from radar data. This approach effectively enriches the feature representation by incorporating spatial relationships compared to traditional methods that rely only on 2D feature extraction. Furthermore, we incorporate a point cloud upsampling task to densify the radar point cloud, rectify point positions, and derive additional 3D features under the guidance of lidar data. Finally, we fuse radar and camera features during the decoding phase for depth estimation. We benchmark our proposed GET-UP on the nuScenes dataset, achieving state-of-the-art performance with a 15.3% and 14.7% improvement in MAE and RMSE over the previously best-performing model. Code: https://github.com/harborsarah/GET-UP