67 citations · 212 across the 10 of their papers we have counts for
10 papers
TMO: Textured Mesh Acquisition of Objects with a Mobile Device by using Differentiable Rendering
Jaehoon Choi, Dongki Jung, Taejae Lee +4
We present a new pipeline for acquiring a textured mesh in the wild with a single smartphone which offers access to images, depth maps, and valid poses. Our method first introduces…
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…
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…
Arbitrary Style Transfer using Graph Instance Normalization
Dongki Jung, Seunghan Yang, Jaehoon Choi +1
Style transfer is the image synthesis task, which applies a style of one image to another while preserving the content. In statistical methods, the adaptive instance normalization…
SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction
Jaehoon Choi, Dongki Jung, Donghwan Lee +1
Self-supervised monocular depth estimation has emerged as a promising method because it does not require groundtruth depth maps during training. As an alternative for the groundtru…