Search Results for author: Leo Lebrat

Found 3 papers, 1 papers with code

Divide and Conquer: Rethinking the Training Paradigm of Neural Radiance Fields

no code implementations29 Jan 2024 Rongkai Ma, Leo Lebrat, Rodrigo Santa Cruz, Gil Avraham, Yan Zuo, Clinton Fookes, Olivier Salvado

Neural radiance fields (NeRFs) have exhibited potential in synthesizing high-fidelity views of 3D scenes but the standard training paradigm of NeRF presupposes an equal importance for each image in the training set.

CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction

1 code implementation NeurIPS 2021 Leo Lebrat, Rodrigo Santa Cruz, Frederic de Gournay, Darren Fu, Pierrick Bourgeat, Jurgen Fripp, Clinton Fookes, Olivier Salvado

In this paper, we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensional image, learns to deform a reference template towards a targeted object.

Surface Reconstruction

DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction

no code implementations22 Oct 2020 Rodrigo Santa Cruz, Leo Lebrat, Pierrick Bourgeat, Clinton Fookes, Jurgen Fripp, Olivier Salvado

Having these limitations in mind, we propose DeepCSR, a 3D deep learning framework for cortical surface reconstruction from MRI.

Surface Reconstruction

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