Lam Huynh — accepted AI-conference papers

2021

Boosting Monocular Depth Estimation With Lightweight 3D Point Fusion

Lam Huynh, Phong Nguyen, Jiří Matas, Esa Rahtu, Janne Heikkilä

ICCV 2021poster

In this paper, we propose enhancing monocular depth estimation by adding 3D points as depth guidance. Unlike existing depth completion methods, our approach performs well on extremely sparse and unevenly distributed point clouds, which makes it agnostic to the source of the 3D points. We achieve thi…

Cited by 27 PDF ↗Scholar ↗

2020

Guiding Monocular Depth Estimation Using Depth-Attention Volume

Lam Huynh, Phong Nguyen-Ha, Jiri Matas, Esa Rahtu, Janne Heikkilä

ECCV 2020poster

Recovering the scene depth from a single image is an ill-posed problem that requires additional priors, often referred to as monocular depth cues, to disambiguate different 3D interpretations. In recent works, those priors have been learned in an end-to-end manner from large datasets by using deep n…

Cited by 193 Source ↗PDF ↗code ↗Scholar ↗