Pytorch implementation for Neural Radiance Field Codebooks (ICLR 2023). See project website for video results.
Compositional representations of the world are a promising step towards enabling high-level scene understanding and efficient transfer to downstream tasks. Learning such representations for complex scenes and tasks remains an open challenge. Towards this goal, we introduce Neural Radiance Field Codebooks ( NRC ), a scalable method for learning object-centric representations through novel view reconstruc- tion. NRC learns to reconstruct scenes from novel views using a dictionary of object codes which are decoded through a volumetric renderer. This enables the discovery of reoccurring visual and geometric patterns across scenes which are transferable to downstream tasks
Pip install -r requirements.txt
To create the Thor dataset to train the model first install ProcThor. Instructions for installing ProcThor can be found here: https://github.com/allenai/procthor. Once ProcThor has been installed run python ThorWalkthrough.py.
Training code is in train/train.py. Training options and hyper-parameters can be found in util/args.py. Original model was trained on 1000 scenes from ProcThor with 10 trajectories through each scene with maximum trajectory length of 200 steps.
python train/train.py -n NRC -c conf/exp/thorL.conf -D <data dir> -V 1 --gpu_id=<GPU> --dict_size 128 --ste
The data directory should be specified as folder containing the frames from the above step.
To evaluate on Object-Navigation:
- Install AllenAct from https://github.com/allenai/allenact.
- Load the saved model from training in the previous step and load it as the pretrained visual network.
- Train the policy network on object-navigation with default hyper-parameters. See AllenAct for further details on training a policy network on the Thor environment. We train for 200 million steps dd-ppo and default hyper-parameters.
Novel view reconstruction can be seen visualized by running:
tensorboard --logdir <project dir>/logs/<expname>.
Log files can be found in /logs/. To visualize loss and reconstruction run
tensorboard --logdir <project dir>/logs/<expname>
This repository was based off the implementation of PixelNeRF: https://github.com/sxyu/pixel-nerf.