GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsampling

TL;DR

GET-UP introduces a geometric-aware radar depth estimation method using attention-enhanced GNNs and point cloud upsampling, significantly improving accuracy in autonomous driving scenarios.

Contribution

The paper presents a novel GNN-based approach that leverages 3D radar geometry and upsampling to enhance depth estimation from radar data.

Findings

Achieves state-of-the-art MAE and RMSE on nuScenes dataset.

Effectively densifies radar point clouds for better feature extraction.

Outperforms previous models by over 14% in key metrics.

Abstract

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