4 papers
SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning
Jaehoon Choi, Dongki Jung, Yonghan Lee +3
Monocular depth estimation in the wild inherently predicts depth up to an unknown scale. To resolve scale ambiguity issue, we present a learning algorithm that leverages monocular…
DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes
Dongki Jung, Jaehoon Choi, Yonghan Lee +4
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. O…
Large-scale Localization Datasets in Crowded Indoor Spaces
Donghwan Lee, Soohyun Ryu, Suyong Yeon +8
Estimating the precise location of a camera using visual localization enables interesting applications such as augmented reality or robot navigation. This is particularly useful in…
SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments
Jaehoon Choi, Dongki Jung, Yonghan Lee +3
We present a novel algorithm for self-supervised monocular depth completion. Our approach is based on training a neural network that requires only sparse depth measurements and cor…