38 citations · 38 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
Investigating the Role of Image Retrieval for Visual Localization -- An exhaustive benchmark
Martin Humenberger, Yohann Cabon, Noé Pion +5
Visual localization, i.e., camera pose estimation in a known scene, is a core component of technologies such as autonomous driving and augmented reality. State-of-the-art localizat…
A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments
Hyungtae Lim, Suyong Yeon, Soohyun Ryu +6
Global registration using 3D point clouds is a crucial technology for mobile platforms to achieve localization or manage loop-closing situations. In recent years, numerous research…
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…