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RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar
2020
2020 IEEE Intelligent Vehicles Symposium (IV)
This paper presents an efficient annotation procedure and an application thereof to end-to-end, rich semantic segmentation of the sensed environment using Frequency-Modulated Continuous-Wave scanning radar. We advocate radar over the traditional sensors used for this task as it operates at longer ranges and is substantially more robust to adverse weather and illumination conditions. We avoid laborious manual labelling by exploiting the largest radar-focused urban autonomy dataset collected to
doi:10.1109/iv47402.2020.9304674
fatcat:fj34rl7tezb6nb3lwepdwzvaam