My course project for MIT's Advances in Computer Vision class.
Clone the repo and execute the following commands from the repository's root.
Create a virtual environment:
python -m venv state_encoder_3d_env
Activate the environment:
source state_encoder_3d_env/bin/activate
Install the state_encoder_3d package in development mode:
pip install -e .
Training data can be generated using scripts/generate_planar_cube_data.py.
All training, evaluation, and visualization scripts can be found in the scripts/ folder.
A detailed report can be found here.
The below figure is taken from the report and compares the performance of different models.
