Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry (CVPR 2022, Oral - Top 4%)
Gwangbin Bae, Ignas Budvytis, Roberto Cipolla
In this paper, we propose MaGNet, a novel framework for fusing single-view depth probability with multi-view geometry, to improve the accuracy, robustness and efficiency of multi-view depth estimation.
Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation (ICCV 2021, Oral - Top 3%)
Gwangbin Bae, Ignas Budvytis, Roberto Cipolla
In this paper, we estimate and evaluate the aleatoric uncertainty in CNN-based surface normal estimation, for the first time in literature. We also introduce a novel decoder framework where pixel-wise MLPs are trained on a subset of pixels selected based on the estimated uncertainty.
Deep Multi-View Stereo for Dense 3D Reconstruction from Monocular Endoscopic Video (MICCAI 2020)
Gwangbin Bae, Ignas Budvytis, Chung-Kwong Yeung, Roberto Cipolla
In this paper, we introduce a deep-learning-based multi-view stereo pipeline that can estimate dense and accurate 3D reconstruction from a sequence of monocular endoscopic images.
Approximate Depth Estimation in Colonoscopy Images
Gwangbin Bae (MPhil Thesis, 2019)
The ability to estimate depth can improve the accuracy and safety of colonoscopy procedures. In this project, we introduce a self-supervised approach where Structure-from-Motion is used to recover sparse ground truth depth up to an arbitrary scale, and the network is trained with a scale-invariant loss.
Inflationary Models: a critical study
Gwangbin Bae (Third Year Project, 2015) - Nikon Prize for the Best Third Year Project
In this project, we evaluate various models of cosmological inflation based on the new Planck result.