Sparse convolutional neural network for high-resolution skull shape completion and shape super-resolution

Traditional convolutional neural network (CNN) methods rely on dense tensors, which makes them suboptimal for spatially sparse data. In this paper, we propose a CNN model based on sparse tensors for efficient processing of high-resolution shapes represented as binary voxel occupancy grids. In contra...

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Detalles Bibliográficos
Autores principales: Li, Jianning, Gsaxner, Christina, Pepe, Antonio, Schmalstieg, Dieter, Kleesiek, Jens, Egger, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10658170/
https://www.ncbi.nlm.nih.gov/pubmed/37981641
http://dx.doi.org/10.1038/s41598-023-47437-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10658170/
https://www.ncbi.nlm.nih.gov/pubmed/37981641
http://dx.doi.org/10.1038/s41598-023-47437-6

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