Leo Lebrat — accepted AI-conference papers

2024

NeRF Director: Revisiting View Selection in Neural Volume Rendering

Wenhui Xiao, Rodrigo Santa Cruz, David Ahmedt-Aristizabal, Olivier Salvado, Clinton Fookes, Leo Lebrat

CVPR 2024poster

Neural Rendering representations have significantly contributed to the field of 3D computer vision. Given their potential considerable efforts have been invested to improve their performance. Nonetheless the essential question of selecting training views is yet to be thoroughly investigated. This ke…

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2021

CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction

Leo Lebrat, Rodrigo Santa Cruz, Frederic de Gournay, Darren Fu, Pierrick Bourgeat, Jurgen Fripp +2

NeurIPS 2021poster

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. To conserve the template mesh’s topological properties, we train our model over a set of diffeomorphic transformations. Th…

Cited by 41 Source ↗PDF ↗code ↗Scholar ↗

2021

MongeNet: Efficient Sampler for Geometric Deep Learning

Leo Lebrat, Rodrigo Santa Cruz, Clinton Fookes, Olivier Salvado

CVPR 2021poster

Recent advances in geometric deep-learning introduce complex computational challenges for evaluating the distance between meshes. From a mesh model, point clouds are necessary along with a robust distance metric to assess surface quality or as part of the loss function for training models. Current m…

Cited by 3 PDF ↗code ↗Scholar ↗