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• W4313496329 abstract "Several SLAM methods benefit from the use of semantic information. Most integrate photometric methods with high-level semantics such as object detection and semantic segmentation. We propose that adding a semantic segmentation decoder in a shared encoder architecture would help the descriptor decoder learn semantic information, improving the feature extractor. This would be a more robust approach than only using high-level semantic information since it would be intrinsically learned in the descriptor and would not depend on the final quality of the semantic prediction. To add this information, we take advantage of multi-task learning methods to improve accuracy and balance the performance of each task. The proposed models are evaluated according to detection and matching metrics on the HPatches dataset. The results show that the Semantic SuperPoint model performs better than the baseline one." @default.
• W4313496329 created "2023-01-06" @default.
• W4313496329 creator A5009669213 @default.
• W4313496329 creator A5074413750 @default.
• W4313496329 creator A5076629697 @default.
• W4313496329 date "2022-10-18" @default.
• W4313496329 modified "2023-09-23" @default.
• W4313496329 title "Semantic SuperPoint: A Deep Semantic Descriptor" @default.
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• W4313496329 doi "https://doi.org/10.1109/lars/sbr/wre56824.2022.9996027" @default.
• W4313496329 hasPublicationYear "2022" @default.
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