Details

Autor(en) / Beteiligte

• Larsson, Mans
• Stenborg, Erik
• Toft, Carl
• Hammarstrand, Lars
• Sattler, Torsten
• Kahl, Fredrik

Titel

Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization

Ist Teil von

• 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, Vol.2019-October (October), p.31-41

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• Long-term visual localization is the problem of estimating the camera pose of a given query image in a scene whose appearance changes over time. It is an important problem in practice that is, for example, encountered in autonomous driving. In order to gain robustness to such changes, long-term localization approaches often use segmantic segmentations as an invariant scene representation, as the semantic meaning of each scene part should not be affected by seasonal and other changes. However, these representations are typically not very discriminative due to the very limited number of available classes. In this paper, we propose a novel neural network, the Fine-Grained Segmentation Network (FGSN), that can be used to provide image segmentations with a larger number of labels and can be trained in a self-supervised fashion. In addition, we show how FGSNs can be trained to output consistent labels across seasonal changes. We show through extensive experiments that integrating the fine-grained segmentations produced by our FGSNs into existing localization algorithms leads to substantial improvements in localization performance.