mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-12 02:45:34 +08:00
Ethan WeberandMatt Tancik
1052fccec2
* start of mesh PR * point cloud export mvp * start of mesh PR * point cloud export mvp * Save out mesh. Poor quality atm * reorganized mesh algorithms and specified CLI * CLI reorg * tsdf algorithm in progress * debugging * tsdf functional but camera distortion params not in * playing with distortion params * TSDF works but texture looks bad * added trimesh * moved to exporter * removed files * saving progress * save texture image * seems to work w/o barycentric * barycentric start * gap between triangles * blocking out diagonal * new barcentric and scaling * meshing working. need to remove extra rows * fixed triangle packing * gitignore changes * removed package * fix Literal import * comments * update .mtl file * poisson mesh with model output normals & resolved some PR comments on git * starting docs. breaking up big function. todo: add xatlas * xatlas and nvdiffrast working * triangle_id * progress on expensive texture coordinates method * pytorch meshing works * removing nvdiffrast * remove trimesh * cleanup * few changes * pred normals by default and add minimum additions to viewer * fix cli in viewer * add xatlas as dependency * make point cloud more dense * reverted viewer changes * turn off normals by default * fix docs * finished todos on github * remove xatlas * xatlas * black Co-authored-by: Matt Tancik <tancik@berkeley.edu>
65 lines
2.1 KiB
Markdown
65 lines
2.1 KiB
Markdown
# Export geometry
|
|
|
|
Here we document how to export point clouds and meshes from nerfstudio. The main command you'll be working with is `ns-export`. Our point clouds are exported as `.ply` files and the textured meshes are exported as `.obj` files.
|
|
|
|
## Exporting a mesh
|
|
|
|
### 1. TSDF Fusion
|
|
|
|
TSDF (truncated signed distance function) Fusion is a meshing algorithm that uses depth maps to extract a surface as a mesh. This method works for all models.
|
|
|
|
```python
|
|
ns-export tsdf --load-config CONFIG.yml --output-dir OUTPUT_DIR
|
|
```
|
|
|
|
### 2. Poisson surface reconstruction
|
|
|
|
Poisson surface reconstruction gives the highest quality meshes. See the steps below to use Poisson surface reconstruction in our repo.
|
|
|
|
:::{admonition} Note
|
|
:class: info
|
|
|
|
This will only work with a Model that computes or predicts normals, e.g., nerfacto.
|
|
:::
|
|
|
|
1. Train nerfacto with network settings that predict normals.
|
|
|
|
```bash
|
|
ns-train nerfacto --pipeline.model.predict-normals True
|
|
```
|
|
|
|
2. Export a mesh with the Poisson meshing algorithm.
|
|
|
|
```bash
|
|
ns-export poisson --load-config CONFIG.yml --output-dir OUTPUT_DIR
|
|
```
|
|
|
|
## Exporting a point cloud
|
|
|
|
```bash
|
|
ns-export pointcloud --help
|
|
```
|
|
|
|
## Other exporting methods
|
|
|
|
Run the folowing command to see other export methods that may exist.
|
|
|
|
```python
|
|
ns-export --help
|
|
```
|
|
|
|
## Texturing an existing mesh with NeRF
|
|
|
|
Say you want to simplify and/or smooth a mesh offline, and then you want to texture it with NeRF. You can do that with the following command. It will work for any mesh filetypes that [PyMeshLab](https://pymeshlab.readthedocs.io/en/latest/) can support, for example a `.ply`.
|
|
|
|
```python
|
|
python scripts/texture.py --load-config CONFIG.yml --input-mesh-filename FILENAME --output-dir OUTPUT_DIR
|
|
```
|
|
|
|
## Dependencies
|
|
|
|
Our dependencies are shipped with the pip package in the pyproject.toml file. These are the following:
|
|
|
|
- [xatlas-python](https://github.com/mworchel/xatlas-python) for unwrapping meshes to a UV map
|
|
- [pymeshlab](https://pymeshlab.readthedocs.io/en/latest/) for reducing the number of faces in a mesh
|