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3DShapeNets

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for cvpr2015 project 3DShapeNets

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# 3D ShapeNets: A Deep Representation for Volumetric Shapes
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## 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](http://3dshapenets.cs.princeton.edu).
- 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!

3DShapeNets

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