A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Generative and Discriminative Voxel Modeling with Convolutional Neural Networks
[article]
2016
arXiv
pre-print
When working with three-dimensional data, choice of representation is key. We explore voxel-based models, and present evidence for the viability of voxellated representations in applications including shape modeling and object classification. Our key contributions are methods for training voxel-based variational autoencoders, a user interface for exploring the latent space learned by the autoencoder, and a deep convolutional neural network architecture for object classification. We address
arXiv:1608.04236v2
fatcat:jurty7fbhbgwfazvjnsin46xk4