PyTorch implementation of nerf2nerf, a framework for robustly registering two NeRFs with respect to a common object of interest.

nerf2nerf: Pairwise Registration of Neural Radiance Fields
Lily Goli, Daniel Rebain, Sara Sabour, Animesh Garg, Andrea Tagliasacchi

To get started quickly, assuming Singularity and NVIDIA drivers/libraries are installed on your device, get the bash script file from here and put it in target directory. Then run the command below in terminal to get the demo running. Please note the script will clone this repository and a Singlarity image for a compatible environment, automatically and there is no need for separate cloning. Tested to work with nvidia-cuda-toolkit 11.0.

./run_demo.sh

You can view resulting images and graphs in your browser on localhost:6006 and track sampling process in 3D on localhost:5946.

You can download the dataset from here and put the 'scenes' directoy right under the main directory.

Set up a conda environment and activate:

conda create -n n2n python=3.10
conda activate n2n
pip install -r requirements.txt
pip install git+https://github.com/jonbarron/robust_loss_pytorch

To run registration code:

python main.py --yaml <object_name> 
# example: python main.py --yaml bust 

The results are saved to tensorboard. Connect to tensorboard to view results. To reobtain the distilled models and run the surface field distillation code:

python distill.py --scene_no <scene_number> --a_or_b <a/b>
# example: python distill.py --scene_no 1 --a_or_b a

If use_vis is enabled in options yaml file, to see the sample points changing during registration in 3D, in a terminal run:

conda activate n2n
visdom -p <port_number>
# example: visdom -p 5946

This will launch visdom on localhost:<port_number>. You can change the port_number in option yaml files.

To see the results, launch tensorboard in terminal:

tensorboard --logdir="." --port=<tensorboard_port_number> --bind_all

Then open a browser window (or alternatively use port forwarding if you are using a remote server) and view results on localhost:<tensorboard_port_number>.