NLOS-NeuS: Non-line-of-sight Neural Implicit Surface

This is the official pytorch implementation of "NLOS-NeuS: Non-line-of-sight Neural Implicit Surface," ICCV2023.

Project Page | Paper | Poster

Requirements

Our experimental environment is

Ubuntu 20.04
CUDA 11.3.1
pytorch 1.11.0

The requirements can be installed by

conda install --name nlos-neus --file requirements.yml
conda activate nlos-neus

Data

Please download the preprocessed data from the NeTF.

Pre-trained models

We provide our pre-trained model here.

tar -zxvf out.tar.gz

Test

We provide several codes for obtaining results.

# render depth saved as npy
python render_depth.py --config configs/test/zaragoza_bunny.txt --test_volume_size 207
# render directional albedo saved as npy
python render_albedo.py --config configs/test/zaragoza_bunny.txt
# extract point cloud and mesh saved as ply
python extract_mesh.py --config configs/test/zaragoza_bunny.txt

These results are saved at recon.

Training

If you want to run training, please run the following for estimating SDF lower bounds beforehand:

python space_carving --scene zaragoza_bunny

Then, please run

python run_netf.py --config configs/zaragoza_bunny.txt

Acknowledgments

This code was heavily built on NeTF_public. We are grateful for their excellent work.