PyTorch implementation of iNeRF, an RGB-only method that inverts neural radiance fields (NeRFs) for 6DoF pose estimation.

iNeRF Inverting Neural Radiance Fields for Pose Estimation
Lin Yen-Chen¹, Pete Florence², Jonathan T. Barron², Alberto Rodriguez¹, Phillip Isola¹, Tsung-Yi Lin^2
1MIT, ²Google
IROS 2021

This preliminary codebase currently only shows how to apply iNeRF with pixelNeRF. However, iNeRF can work with the original NeRF as well.

To start, create the environment using conda:

cd pixel-nerf
conda env create -f environment.yml
conda activate pixelnerf
pip install mediapy
pip install jupyter

Please make sure you have up-to-date NVIDIA drivers supporting CUDA 10.2 at least.

1. Download all pixelNeRF's pretrained weight files from here. Extract this to ./pixel-nerf/checkpoints/, so that ./pixel-nerf/checkpoints/srn_car/pixel_nerf_latest exists.

2. Launch the Jupyter notebook.

cd pixel-nerf
jupyter notebook

3. Open pose_estimation.ipynb and run through it. You can preview the results here. In the following, we show the overlay of images rendered with our predicted poses and the target image.

@inproceedings{yen2020inerf,
  title={{iNeRF}: Inverting Neural Radiance Fields for Pose Estimation},
  author={Lin Yen-Chen and Pete Florence and Jonathan T. Barron and Alberto Rodriguez and Phillip Isola and Tsung-Yi Lin},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems ({IROS})},
  year={2021}
}

This implementation is based on Alex Yu's pixel-nerf.