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jsnln / WNNC

Winding Number Normal Consistency
MIT License
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Winding Number Normal Consistency (WNNC)

This repository contains the official implementation of the paper

Fast and Globally Consistent Normal Orientation based on the Winding Number Normal Consistency

ACM ToG 2024, SIGGRAPH Asia 2024 Journal Track

Siyou Lin, Zuoqiang Shi, Yebin Liu

Project page | Paper

WNNC

Features

  1. A PyTorch extension for accelerating winding numbers in ext/wn_treecode
  2. A fast iterative method for normal estimation: main_wnnc.py WNNC gallery
  3. As a by-product, we provide unofficial CPU and CUDA implementations for Gauss surface reconstruction in ext/gaussrecon_src, which is faster than PoissonRecon with better smoothness control, but never officially open-sourced.

Usage

  1. For WNNC normal estimation: 
    
cd ext
pip install -e .
cd ..

width is important for normal quality, we provide a few presets through --width_config

for clean uniform samples, use l0

python main_wnnc.py data/Armadillo_40000.xyz --width_config l0 --tqdm

for noisy or non-uniform points, use configs l1 (small noise) ~ l5 (large noise) depending on the noise level

a higher level gives smoother normals and better resilience to noise

python main_wnnc.py data/bunny_noised.xyz --width_config l1 --tqdm ... python main_wnnc.py data/bunny_noised.xyz --width_config l5 --tqdm

the user can also use custom widths:

python main_wnnc.py data/bunny_noised.xyz --width_config custom --wsmin 0.03 --wsmax 0.12 --tqdm

to see a complete list of options:

python main_wnnc.py -h


2. For Gauss surface reconstruction:
First download [ANN 1.1.2](https://www.cs.umd.edu/~mount/ANN/) and unpack to `ext/gaussrecon_src/ANN`. Run `make` there. Then go back to the main repository directory, and:
```bash
sh build_GR_cpu.sh
sh build_GR_cuda.sh

./main_GaussReconCPU -i <input.xyz> -o <output.ply>
./main_GaussReconCUDA -i <input.xyz> -o <output.ply>

TODO

This is a preliminary version and updates are coming soon