Selena Ling,
Abhishek Madan,
Nicholas Sharp
Alec Jacobson
Eurographics Symposium on Geometry Processing 2025
We provide the code as a library. To install, you need to clone this repo, and run
pip install .
Then to acquire a uniform set of samples with your implicit surface function in your scripts, you can follow this example:
from ImplicitUniformSampler import ImplicitUniformSampler
sampler = ImplicitUniformSampler()
samples = sampler.sample(sdf_func, num_rays=2000)
# Optional: save sampled points to local.
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(samples.detach().cpu().numpy())
o3d.io.write_point_cloud("outputs.ply", pcd)
We provide an example of using our library with SDFStudio in the submodule example/sdfstudio.
To run this example, you need to set up the enviroment following instructions in their official repo, and run
git submodule update --init --recursive
cd example/sdfstudio
pip install .
ns-download-data sdfstudio
Download a pre-trained checkpoint from here and store it under ./outputs.
And finally run
ns-export uniformsamples --load-config outputs/data-sdfstudio-demo-data-dtu-scan65/neus/2025-02-21_143619/config.yml --output-dir outputs/data-sdfstudio-demo-data-dtu-scan65/neus/2025-02-21_143619
to get the sampled point cloud as a .ply file.