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Continual Learning for Image-Based Camera Localization
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en
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11
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2021 International Conference on Computer Vision, ICCV, pp. 3232-3242, 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.
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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