# Generative and Discriminative Voxel Modeling with Convolutional Neural Networks

**Authors:** Andrew Brock, Theodore Lim, J. M. Ritchie, Nick Weston  
**arXiv:** [1608.04236](https://arxiv.org/abs/1608.04236)  
**Date:** 2016-08-15  
**Summary page:** https://www.summarizepaper.com/fr/paper/1608.04236v2/

## 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.