Semantic SuperPoint: A Deep Semantic Descriptor
This work addresses the need for more robust feature extraction in SLAM systems, though it appears incremental as it builds on existing methods like SuperPoint.
The authors tackled the problem of improving feature extractors in SLAM by integrating semantic segmentation into a shared encoder architecture, resulting in a Semantic SuperPoint model that outperforms the baseline on the HPatches dataset.
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.