DARES: Depth Anything in Robotic Endoscopic Surgery with Self-supervised Vector-LoRA of the Foundation Model

1 code implementation • 30 Aug 2024 • Mona Sheikh Zeinoddin, Chiara Lena, Jiongqi Qu, Luca Carlini, Mattia Magro, Seunghoi Kim, Elena De Momi, Sophia Bano, Matthew Grech-Sollars, Evangelos Mazomenos, Daniel C. Alexander, Danail Stoyanov, Matthew J. Clarkson, Mobarakol Islam

To tackle this issue, we introduce Depth Anything in Robotic Endoscopic Surgery (DARES), a novel approach that employs a new adaptation technique, Vector Low-Rank Adaptation (Vector-LoRA) on the DAM V2 to perform self-supervised monocular depth estimation in RAS scenes.

3D Reconstruction Monocular Depth Estimation +1

Tackling Structural Hallucination in Image Translation with Local Diffusion

1 code implementation • 9 Apr 2024 • Seunghoi Kim, Chen Jin, Tom Diethe, Matteo Figini, Henry F. J. Tregidgo, Asher Mullokandov, Philip Teare, Daniel C. Alexander

We hypothesize such hallucinations result from local OOD regions in the conditional images.

Hallucination Image Generation +1

A 3D Conditional Diffusion Model for Image Quality Transfer -- An Application to Low-Field MRI

1 code implementation • 11 Nov 2023 • Seunghoi Kim, Henry F. J. Tregidgo, Ahmed K. Eldaly, Matteo Figini, Daniel C. Alexander

Low-field (LF) MRI scanners (<1T) are still prevalent in settings with limited resources or unreliable power supply.

AGCN: Adversarial Graph Convolutional Network for 3D Point Cloud Segmentation

2 code implementations • British Machine Vision Conference (BMVC) 2021 • Seunghoi Kim, Daniel C. Alexander

To overcome these problems, we propose a) a graph convolutional network (GCN) in an adversarial learning scheme where a discriminator network provides a segmentation network with informative information to improve segmentation accuracy and b) a graph convolution, GeoEdgeConv, as a means of local feature aggregation to improve segmentation accuracy and space and time complexities.

3D Part Segmentation Point Cloud Segmentation +1