A Tensorflow Implementation of VoxNet (http://dimatura.net/research/voxnet/).
Pre-trained weights are provided under the checkpoints directory.
This network is pretty small, under 1 million parameters, which is less than 10MB in size.
A pre-voxelized ShapeNet40 (http://modelnet.cs.princeton.edu/) is already provided in volumetric_data.zip.
It is directly converted from the matlab files included in (http://vision.princeton.edu/projects/2014/3DShapeNets/3DShapeNetsCode.zip) for fairness.
Each example is a .npy file. To visualize a voxel .npy file, go to (http://bkys.io/voxvis).
No dropout is used for faster training.
Batch normalization is used after every layer, except the last fully connected layer.
Network is all-convolutional.
Python (>= 2.7 or >= 3), Tensorflow (>= 1.0) and numpy (>= 1.0).
Run voxnet_train.py to train the model. (You can skip this step if you trust the pre-trained model)
Run voxnet_test.py to test the average accuracy.
If you want to test on a single file, you have to extract it from the volumetric_data.zip and modify the code.
After 20 minutes of training from scratch on a Nvidia Titan X, it is able to get 86%+ test accuracy.