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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.

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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.

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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.