%0 Generic %1 lebrat2021mongenet %A Lebrat, Léo %A Cruz, Rodrigo Santa %A Fookes, Clinton %A Salvado, Olivier %D 2021 %K 2021 deep-learning geometry sampling %T MongeNet: Efficient Sampler for Geometric Deep Learning %U http://arxiv.org/abs/2104.14554 %X 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 methods often rely on a uniform random mesh discretization, which yields irregular sampling and noisy distance estimation. In this paper we introduce MongeNet, a fast and optimal transport based sampler that allows for an accurate discretization of a mesh with better approximation properties. We compare our method to the ubiquitous random uniform sampling and show that the approximation error is almost half with a very small computational overhead.

@misc{lebrat2021mongenet, abstract = {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 methods often rely on a uniform random mesh discretization, which yields irregular sampling and noisy distance estimation. In this paper we introduce MongeNet, a fast and optimal transport based sampler that allows for an accurate discretization of a mesh with better approximation properties. We compare our method to the ubiquitous random uniform sampling and show that the approximation error is almost half with a very small computational overhead.}, added-at = {2021-05-06T14:43:25.000+0200}, author = {Lebrat, Léo and Cruz, Rodrigo Santa and Fookes, Clinton and Salvado, Olivier}, biburl = {https://www.bibsonomy.org/bibtex/20475510902152b7e0e3ffd9333ab9a20/analyst}, description = {[2104.14554] MongeNet: Efficient Sampler for Geometric Deep Learning}, interhash = {ab76c6069b7d5fda963ed34654d4f7d0}, intrahash = {0475510902152b7e0e3ffd9333ab9a20}, keywords = {2021 deep-learning geometry sampling}, note = {cite arxiv:2104.14554}, timestamp = {2021-05-06T14:43:25.000+0200}, title = {MongeNet: Efficient Sampler for Geometric Deep Learning}, url = {http://arxiv.org/abs/2104.14554}, year = 2021 }