We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ajbrock/Generative-and-Discriminative-Voxel-Modeling | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| jitter_chunk | Ran | ajbrock/Generative-and-Discriminative-Voxel-Modeling/Generative/train_VAE.py code served (permissive licence) · get_code("3ac47877c7c45cb0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1608.04236")
get_code_for_paper("1608.04236")
have("1608.04236")
Connect an agent — have() is free.