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- W4200215237 abstract "Visual place localization (VPL) is a problem finding the closest database image from a query image. Since the outdoor images can be recognized from GPS sensors, VPL in an indoor scene is a difficult problem. Also, Image changes indoors are more severe than outdoors. It is because the position of objects can be easily changed indoors. To tackle this problem, we propose a novel localization dataset with 3D objects considering their physical locations in a scene and encode semantic information using neural networks. Experimental results show that our proposed method outperforms other baseline methods on our localization dataset." @default.
- W4200215237 created "2021-12-31" @default.
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- W4200215237 date "2021-10-12" @default.
- W4200215237 modified "2023-10-18" @default.
- W4200215237 title "Semantic Descriptors into Representation for Robust Indoor Visual Place Recognition" @default.
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- W4200215237 doi "https://doi.org/10.23919/iccas52745.2021.9649944" @default.
- W4200215237 hasPublicationYear "2021" @default.
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