论文标题
语义超级点：深层语义描述符
Semantic SuperPoint: A Deep Semantic Descriptor
论文作者
论文摘要
使用语义信息的几种SLAM方法受益。大多数将光度法与高级语义（例如对象检测和语义分割）相结合。我们建议在共享编码器体系结构中添加语义分割解码器将有助于描述符解码器学习语义信息，从而改善功能提取器。这将是一种比仅使用高级语义信息更强大的方法，因为它将在描述符中本质上学习，并且不取决于语义预测的最终质量。为了添加此信息，我们利用多任务学习方法来提高准确性并平衡每个任务的性能。根据HPATCHES数据集上的检测和匹配指标对所提出的模型进行评估。结果表明，语义超级点模型的性能优于基线模型。
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.
