Lightweight input adapters preprocess images to match ideal-condition training data for off-the-shelf CV models, enabling self-supervised incremental adaptation and reported gains in segmentation and localization on RobotCar and BDD datasets.
Improving Nighttime Retrieval-Based Localization
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abstract
Outdoor visual localization is a crucial component to many computer vision systems. We propose an approach to localization from images that is designed to explicitly handle the strong variations in appearance happening between daytime and nighttime. As revealed by recent long-term localization benchmarks, both traditional feature-based and retrieval-based approaches still struggle to handle such changes. Our novel localization method combines a state-of-the-art image retrieval architecture with condition-specific sub-networks allowing the computation of global image descriptors that are explicitly dependent of the capturing conditions. We show that our approach improves localization by a factor of almost 300\% compared to the popular VLAD-based methods on nighttime localization.
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cs.CV 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Don't Worry About the Weather: Unsupervised Condition-Dependent Domain Adaptation
Lightweight input adapters preprocess images to match ideal-condition training data for off-the-shelf CV models, enabling self-supervised incremental adaptation and reported gains in segmentation and localization on RobotCar and BDD datasets.