Generative and Discriminative Voxel Modeling with Convolutional Neural Networks

Andrew Brock; Theodore Lim; J. M. Ritchie; Nick Weston

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arXiv · 1608.04236

Generative and Discriminative Voxel Modeling with Convolutional Neural Networks

Andrew Brock·Theodore Lim·J. M. Ritchie·Nick Weston

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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.

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BibTeX RIS

Andrew Brock, Theodore Lim, J. M. Ritchie, Nick Weston. 2016-08-15. Generative and Discriminative Voxel Modeling with Convolutional Neural Networks. https://arxiv.org/abs/1608.04236

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