Semantic SuperPoint: A Deep Semantic Descriptor

  • Gabriel Soares Gama USP
  • Nicolas Dos Santos Rosa USP
  • Valdir Grassi USP


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.
Palavras-chave: Measurement, Simultaneous localization and mapping, Semantic segmentation, Semantics, Object detection, Feature extraction, Multitasking, Computer Vision, Visual Odometry
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GAMA, Gabriel Soares; ROSA, Nicolas Dos Santos; GRASSI, Valdir. Semantic SuperPoint: A Deep Semantic Descriptor. In: SIMPÓSIO BRASILEIRO DE ROBÓTICA E SIMPÓSIO LATINO AMERICANO DE ROBÓTICA (SBR/LARS), 19. , 2022, São Bernardo do Campo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 294-299.