https://github.com/elerac/strand_integration
[PG2023] Refinement of Hair Geometry by Strand Integration
https://github.com/elerac/strand_integration
3d-reconstruction hair mvs strand
Last synced: 9 months ago
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[PG2023] Refinement of Hair Geometry by Strand Integration
- Host: GitHub
- URL: https://github.com/elerac/strand_integration
- Owner: elerac
- Created: 2023-08-31T03:18:35.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-30T03:34:54.000Z (about 1 year ago)
- Last Synced: 2025-03-24T13:21:26.059Z (10 months ago)
- Topics: 3d-reconstruction, hair, mvs, strand
- Language: C++
- Homepage: https://elerac.github.io/strand_integration_page/
- Size: 55 MB
- Stars: 28
- Watchers: 3
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Strand Integration (PG2023)
> [**Refinement of Hair Geometry by Strand Integration**](https://elerac.github.io/strand_integration_page),
> Ryota Maeda, Kenshi Takayama, Takafumi Taketomi,
> Computer Graphics Forum (PG2023)
This repository contains the following implementations:
- **Strand Integration**: The official implementation of our paper "Refinement of Hair Geometry by Strand Integration" (PG2023).
- **LPMVS**: The unofficial implementation of LPMVS (Line-based PatchMatch MVS) proposed in the paper "Strand-accurate Multi-view Hair Capture" (CVPR2019). And we also provide the implementation of 3D line filtering.
## Multi-view data
### Small-scale data
This repository contains small-scale multi-view images of straight hair for debugging our code. This data is identical to the data used in our paper except that it is resized to 1/10. You can see it in [data/straight_s](data/straight_s).
### Full-scale data
You can download the full-scale multi-view images from [release page](https://github.com/elerac/strand_integration/releases/tag/data).
## Setup
Our code requires to install the our custom Python extension implemented in C++. Please build and install it before running our code.
```bash
cd cpp_ext
pip install .
```
For more details, please refer to [cpp_ext](cpp_ext/).
## Running
```bash
# LPMVS
python run_lpmvs.py data/straight_s -o result/lpmvs/straight_s
# Estimate a 3D line map for each view from multi-view images.
# Run 3D line filtering (for LPMVS)
python run_line_filtering.py result/lpmvs/straight_s result/merged_ply/lpmvs/straight_s.ply
# Merge the 3D line maps into a single point cloud file.
# 3D line consistency map
python run_consistency_map.py result/lpmvs/straight_s -o result/consistency/straight_s
# Generate a 3D line consistency map described in Subsection 3.5 in our paper.
# Strand Integration
python run_strand_integration.py result/lpmvs/straight_s --consistency result/consistency/straight_s -o result/si/straight_s --views 5 --imshow
# This will only apply for view 5. You can run for all views by removing the --views option.
# 3D line filtering (for Strand Integration)
python run_line_filtering.py result/si/straight_s result/merged_ply/si/straight_s.ply
# After running all views, you can merge the results same as the above process.
```
After running the reconstruction codes, you can visualize the .ply files as follows.
```bash
# Visualize ply files
python visualize_ply.py result/merged_ply/si/straight_s.ply --gamma 0.45
```
## BibTeX
```bibtex
@article{maeda2023refinement,
author = {Maeda, Ryota and Takayama, Kenshi and Taketomi, Takafumi},
title = {Refinement of Hair Geometry by Strand Integration},
journal = {Computer Graphics Forum (proceedings of Pacific Graphics)},
volume = {42},
number = {7},
year = {2023}
}
```