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

Abstract 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....

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Bibliografiske detaljer

Principais autores:	Jianning Li, Christina Gsaxner, Antonio Pepe, Dieter Schmalstieg, Jens Kleesiek, Jan Egger
Format:	Artigo
Sprog:	Inglês
Udgivet:	Nature Portfolio 2023-11-01
Serier:	Scientific Reports
Online adgang:	https://doi.org/10.1038/s41598-023-47437-6
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