Depth Estimation from Monocular Images and Sparse Radar Data
This work addresses depth estimation for autonomous driving systems by combining camera and radar data, which is an incremental improvement over existing fusion methods.
The paper tackles the problem of improving depth estimation accuracy by fusing monocular images with sparse radar data using a deep neural network, and demonstrates that their method outperforms existing fusion approaches on the nuScenes dataset.
In this paper, we explore the possibility of achieving a more accurate depth estimation by fusing monocular images and Radar points using a deep neural network. We give a comprehensive study of the fusion between RGB images and Radar measurements from different aspects and proposed a working solution based on the observations. We find that the noise existing in Radar measurements is one of the main key reasons that prevents one from applying the existing fusion methods developed for LiDAR data and images to the new fusion problem between Radar data and images. The experiments are conducted on the nuScenes dataset, which is one of the first datasets which features Camera, Radar, and LiDAR recordings in diverse scenes and weather conditions. Extensive experiments demonstrate that our method outperforms existing fusion methods. We also provide detailed ablation studies to show the effectiveness of each component in our method.