Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization
Paper in proceedings, 2019

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, 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 limited number of available classes. In this paper, we propose a new 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 demonstrate through extensive experiments that integrating the fine-grained segmentations produced by our FGSNs into existing localization algorithms leads to substantial improvements in localization performance.

Author

Måns Larsson

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Erik Stenborg

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

Carl Toft

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Lars Hammarstrand

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Signal Processing

Torsten Sattler

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Fredrik Kahl

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Proceedings of the IEEE International Conference on Computer Vision

1550-5499 (ISSN)

October 31-41

IEEE International Conference on Computer Vision
Seoul, South Korea,

Perceptron

VINNOVA, 2017-06-01 -- 2019-11-30.

Areas of Advance

Information and Communication Technology

Subject Categories

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/ICCV.2019.00012

More information

Latest update

5/25/2020