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SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning
Given these metric poses and monocular sequences, we propose a self-supervised learning method for the pre-trained supervised monocular depth networks to enable metrically scaled depth estimation.
DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes
We present a novel approach for estimating depth from a monocular camera as it moves through complex and crowded indoor environments, e. g., a department store or a metro station.
Large-scale Localization Datasets in Crowded Indoor Spaces
In this paper, we introduce 5 new indoor datasets for visual localization in challenging real-world environments.
SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments
We present a novel algorithm for self-supervised monocular depth completion.
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