Generative and Discriminative Voxel Modeling with Convolutional Neural Networks

Andrew Brock, Theodore Lim, James Millar Ritchie, Nicholas J. Weston

Research output: Contribution to conferenceOtherpeer-review

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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.
Original languageEnglish
Pages1-9
Number of pages9
Publication statusPublished - 5 Dec 2016
EventNeural Inofrmation Processing Conference: 3D Deep Learning - Barcelona, Barcelona, Spain
Duration: 5 Dec 201610 Dec 2016
https://nips.cc/Conferences/2016

Conference

ConferenceNeural Inofrmation Processing Conference
Abbreviated titleNIPS
Country/TerritorySpain
CityBarcelona
Period5/12/1610/12/16
Internet address

Keywords

  • ModelNet benchmark
  • 3D Data
  • Convolutional Neural Networks
  • Variational Autoencoders

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