Generative and Discriminative Voxel Modeling with Convolutional Neural Networks

Code

• github.com/ajbrock/Generative-and-Discriminative-Voxel-Modeling
  OfficialIn papernone★ 0
• github.com/CPUFronz/CapsVoxGAN
  pytorch★ 0

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.

Tasks

Benchmark Results

Reproductions