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.
- Pub Date:
- August 2016
- Computer Science - Computer Vision and Pattern Recognition;
- Computer Science - Human-Computer Interaction;
- Computer Science - Machine Learning;
- Statistics - Machine Learning
- 9 pages, 5 figures, 2 tables