Continual Learning for Image-Based Camera Localization

Loading...
Thumbnail Image

Access rights

openAccess

URL

Journal Title

Journal ISSN

Volume Title

A4 Artikkeli konferenssijulkaisussa

Date

2022

Major/Subject

Mcode

Degree programme

Language

en

Pages

11

Series

2021 International Conference on Computer Vision, ICCV, IEEE International Conference on Computer Vision

Abstract

For several emerging technologies such as augmented reality, autonomous driving and robotics, visual localization is a critical component. Directly regressing camera pose/3D scene coordinates from the input image using deep neural networks has shown great potential. However, such methods assume a stationary data distribution with all scenes simultaneously available during training. In this paper, we approach the problem of visual localization in a continual learning setup -- whereby the model is trained on scenes in an incremental manner. Our results show that similar to the classification domain, non-stationary data induces catastrophic forgetting in deep networks for visual localization. To address this issue, a strong baseline based on storing and replaying images from a fixed buffer is proposed. Furthermore, we propose a new sampling method based on coverage score (Buff-CS) that adapts the existing sampling strategies in the buffering process to the problem of visual localization. Results demonstrate consistent improvements over standard buffering methods on two challenging datasets -- 7Scenes, 12Scenes, and also 19Scenes by combining the former scenes.

Description

Keywords

Other note

Citation

Wang, S, Laskar, Z, Melekhov, I, Li, X & Kannala, J 2022, Continual Learning for Image-Based Camera Localization . in 2021 International Conference on Computer Vision, ICCV . IEEE International Conference on Computer Vision, IEEE, pp. 3232-3242, International Conference on Computer Vision, Virtual, Online, 11/10/2021 . https://doi.org/10.1109/ICCV48922.2021.00324