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Depth-CVAE

PyTorch implementation of a conditional variational autoencoder for predicting depth from images.

Requirements

PyTorch

Choose your relevent PyTorch version here https://pytorch.org/get-started/locally/, by choosing correct system, pip/conda, GPU/CPU only. E.g for Linux using pip with no GPU, this would be

pip3 install torch==1.9.1+cpu torchvision==0.10.1+cpu torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html

Additional requirements

Download the additional requirements by

pip3 install -r requirements.txt

Datasets

VAROS

Download the VAROS dataset from https://zenodo.org/record/5567209#.YcEgMVPMJhE, and place it in the datasets/ folder.

Example of a valid folder structure:

VAROS_ROOT
|__ 2021-08-17_SEQ1
    |__ train
        |__ vehicle0
            |__cam0
                |__A
                |__B
                |__C
                |__D
|__ ...

Configuration

Configuration files are located in configs/, where you can set parameters, location of trained model, demo images etc.

Train

After downloading and placing the datasets correctly, do e.g.

python3 train.py configs/varos.yaml

to train on the VAROS dataset.

Testing

After having trained a model, do e.g.

python3 demo.py configs/varos.yaml

to test on a set of demo images located in demo/

Acknowledgements

Implementation is based on https://github.com/lufficc/SSD.