3D ShapeNets: A Deep Representation for Volumetric Shapes

Introduction

The code implements a Convolution Deep Belief Network in 3D and apply it for 2.5D depth object recognition, 3D shape completion, NBV selection and etc.
To run the code, a CUDA supported GPU should be installed.
The users should make sure the depth rendering function RenderMex is working properly. It depends on openGL. We suggest to add $MATLAB_HOME/sys/opengl/lib/glnxa64/libGL.so.1 to LD_LIBRARY_PATH.
We have tested our code on Ubuntu 12.04 and 14.04, Matlab R2013a and after.

Code

Training 3D ShapeNets involves a pretraining phase (run_pretrainin.m) and a finetuning phase (run_finetuning.m). Generative finetuning is time consuming and sometimes can just slightly improve the performance. You can probably ignore it.

Here is how the code is organized:

1. The root folder contains interfaces for training and testing.
2. The folder "generative" is for probablistic CDBN training.
3. The folder "bp" does discriminative fine-tuning for 3D mesh classification and retrieval.
4. We provide a 3D cuda convolution routine based on [cuda-convnet](https://code.google.com/p/cuda-convnet/) developed by Alex Krizhevsky. They are in kFunction.cu and kFunction2.cu.  
5. The folder "3D" involves 3D computations like TSDF and Rendering.
6. The folder "voxelization" is a toolbox to convert the mesh model to a volume representation.

After training, the model could be powerful to do these tasks:

1. 2.5D object recognition. Given a depth map of an object, infer the category.
2. Shape completion. Given a depth map of an object, infer the full 3D shape.
3. Next-best-view prediction. Compute the recognition uncertainties for the current view and decide the Next-Best-View and move the camera.
4. Discriminative feature learning. Features for 3D meshes learned from volumes can be used for classification and retrieval.

Models

We provide our generative 3D ShapeNets model as well as discriminative finetuned models which should produce the exact result for mesh classification and retrieval in the paper.

Data

The original off mesh data can be downloaded at project page.
The input of 3D ShapeNets is volumetric shapes. One needs to convert the mesh representation into volumes. We provide a function utils/write_input_data.m to produce these volumes from meshes.
We also precomputed some volumes with size 30 under the directory volumetric_data.

Citing 3DShapeNets

If you use our code in your research, please consider citing:

@inproceedings{Zhirong15CVPR,
        Author = {Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, J. Xiao},
        Title = {3D ShapeNets: A Deep Representation for Volumetric Shapes},
        Booktitle = {Computer Vision and Pattern Recognition},
        Year = {2015}
}

Contact

Please email Zhirong Wu (xavibrowu@gmail.com) for problems and bugs. Thanks!

zhirongw / 3DShapeNets