%0 Generic %1 brock2016generative %A Brock, Andrew %A Lim, Theodore %A Ritchie, J. M. %A Weston, Nick %D 2016 %K 2016 3D GAN arxiv deep-learning paper voxel %T Generative and Discriminative Voxel Modeling with Convolutional Neural Networks %U http://arxiv.org/abs/1608.04236 %X 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 challenges unique to voxel-based representations, and empirically evaluate our models on the ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the state of the art for object classification.

@misc{brock2016generative, abstract = {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 challenges unique to voxel-based representations, and empirically evaluate our models on the ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the state of the art for object classification.}, added-at = {2018-06-30T07:36:51.000+0200}, author = {Brock, Andrew and Lim, Theodore and Ritchie, J. M. and Weston, Nick}, biburl = {https://www.bibsonomy.org/bibtex/21b44c0079735fe90be67e3f397e56edf/analyst}, description = {[1608.04236] Generative and Discriminative Voxel Modeling with Convolutional Neural Networks}, interhash = {25742ac97bb5a1953b586c058d245ed7}, intrahash = {1b44c0079735fe90be67e3f397e56edf}, keywords = {2016 3D GAN arxiv deep-learning paper voxel}, note = {cite arxiv:1608.04236Comment: 9 pages, 5 figures, 2 tables}, timestamp = {2018-06-30T07:36:51.000+0200}, title = {Generative and Discriminative Voxel Modeling with Convolutional Neural Networks}, url = {http://arxiv.org/abs/1608.04236}, year = 2016 }