Generative Shape Synthesis with Variational Auto-Decoder

This project by Baris Zöngür and Munzer Dwedari is part of the 'Advanced Deep Learning for Computer Vision' course at the chair of Prof. Dr. Niessner at Technical University of Munich. Both team members contributed equally. You can view our final report here.

Requirements

• Python 3
• Install dependencies from requirements.txt

pip install -r requirements.txt

• Install chamferdist package by krrish94

pip:

pip install chamferdist

conda: download the repository to the root of this project, then run python setup.py install that is inside the folder chamferdist/
Note: we only tested it with conda

Data

• Download Shapenet dataset and unzip the file under data/ Your directory should then look like this:

data/
    shapenet_dim32_df/
        02691156/...
        02747177/...
        ...
    splits/
        shapenet/
            airplane_test.txt
            airplane_train.txt
            ...

Training a model

Variational Auto Decoder

To train a variational auto-decoder use the following command

python scripts/train.py --var <experiment_name> <class>

Available classes: car, airplane, chair, sofa, lamp, cabinet, watercraft, table

Non-Variational Auto Decoder

To train a non-variational auto-decoder, use the following command

python scripts/train.py <experiment_name> <class>

Available classes: same as above

Testing a model

Variational Auto Decoder

To test a trained variational auto-decoder on the validation data, use the following command

python scripts/train.py --var --test <experiment_name> <class> 

Non-Variational Auto Decoder

To test a trained non-variational auto-decoder on the validation data use the following command

python scripts/train.py --test <experiment_name> <class> 

Evaluating a model

Variational Auto Decoder

To evaluate a variational auto decoder on the 1-NN score, use the following command

python scripts/evaluate.py --split test <experiment_name> <class> 1NN 

If you wish to test with a fewer number of samples from the reference set, use the --n 200 flag

Non-Variational Auto Decoder

To evaluate a non-variational auto decoder on the IOU score, use the following command

python scripts/evaluate.py --split test <experiment_name> <class> IOU 

split: train, val

Visualizations

For visulizing samples from shape synthesis, inter-class and intra-class interpolation we prepared a jupyter notebook visualize.ipynb.

Logging

You can use tensorboard to see the losses (under logs/) during training and testing.

tensorboard --logdir logs