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• W3089778819 abstract "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." @default.
• W3089778819 created "2020-10-08" @default.
• W3089778819 creator A5001254143 @default.
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• W3089778819 date "2020-09-30" @default.
• W3089778819 modified "2023-10-17" @default.
• W3089778819 title "Depth Estimation from Monocular Images and Sparse Radar Data" @default.
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• W3089778819 doi "https://doi.org/10.48550/arxiv.2010.00058" @default.
• W3089778819 hasPublicationYear "2020" @default.
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