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- W3011899221 abstract "We propose a method for off-road drivable area extraction using 3D LiDAR data with the goal of autonomous driving application. A specific deep learning framework is designed to deal with the ambiguous area, which is one of the main challenges in the off-road environment. To reduce the considerable demand for human-annotated data for network training, we utilize the information from vast quantities of vehicle paths and auto-generated obstacle labels. Using these autogenerated annotations, the proposed network can be trained using weakly supervised or semi-supervised methods, which can achieve better performance with fewer human annotations. The experiments on our dataset illustrate the reasonability of our framework and the validity of our weakly and semi-supervised methods." @default.
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- W3011899221 date "2020-03-10" @default.
- W3011899221 modified "2023-09-25" @default.
- W3011899221 title "Off-Road Drivable Area Extraction Using 3D LiDAR Data" @default.
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- W3011899221 doi "https://doi.org/10.48550/arxiv.2003.04780" @default.
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