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Update sphinx docs (#1695)

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Matthew Tancik 2023-04-04 11:08:09 -07:00 committed by GitHub
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21
docs/quickstart/custom_dataset.md
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@ -20,6 +20,7 @@ We Currently support the following custom data types:
| 📱 [KIRI Engine](kiri) | IOS or Android | [KIRI Engine App](https://www.kiriengine.com/) | 🐇 |
| 📱 [Record3D](record3d) | IOS with LiDAR | [Record3D app](https://record3d.app/) | 🐇 |
| 🖥 [Metashape](metashape) | Any | [Metashape](https://www.agisoft.com/) | 🐇 |
| 🖥 [RealityCapture](realitycapture) | Any | [RealityCapture](https://www.capturingreality.com/realitycapture) | 🐇 |
(images_and_video)=
@ -286,6 +287,26 @@ ns-process-data metashape --data {data directory} --xml {xml file} --output-dir
ns-train nerfacto --data {output directory}
```
(realitycapture)=
## RealityCapture
1. Align your images using RealityCapture. `ALIGNMENT -> Align Images`
2. Export the camera alignment as a `csv` file. Choose `Internal/External camera parameters`
3. Convert the data to the nerfstudio format.
```bash
ns-process-data realitycapture --data {data directory} --csv {csv file} --output-dir {output directory}
```
4. Train with nerfstudio!
```bash
ns-train nerfacto --data {output directory}
```
(360_data)=
## 360 Data (Equirectangular)

21
docs/reference/cli/index.md
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@ -6,21 +6,22 @@ We provide a command line interface for training your own NeRFs (no coding neces
Here are the popular commands that we offer. If you've cloned the repo, you can also look at the [pyproject.toml file](https://github.com/nerfstudio-project/nerfstudio/blob/main/pyproject.toml) at the `[project.scripts]` section for details.
| Command | Description | Filename |
| ---------------- | -------------------------------------- | ---------------------------------- |
| ns-install-cli | Install tab completion for all scripts | scripts/completions/install.py |
| ns-process-data | Generate a dataset from your own data | scripts/process_data.py |
| ns-download-data | Download existing captures | scripts/downloads/download_data.py |
| ns-train | Generate a NeRF | scripts/train.py |
| ns-viewer | View a trained NeRF | scripts/viewer/run_viewer.py |
| ns-eval | Run evaluation metrics for your Model | scripts/eval.py |
| ns-render | Render out a video of your NeRF | scripts/render.py |
| ns-export | Export a NeRF into other formats | scripts/exporter.py |
| Command | Description | Filename |
| ------------------------------------ | -------------------------------------- | ---------------------------------- |
| [ns-install-cli](ns_install_cli) | Install tab completion for all scripts | scripts/completions/install.py |
| [ns-process-data](ns_process_data) | Generate a dataset from your own data | scripts/process_data.py |
| [ns-download-data](ns_download_data) | Download existing captures | scripts/downloads/download_data.py |
| [ns-train](ns_train) | Generate a NeRF | scripts/train.py |
| [ns-viewer](ns_viewer) | View a trained NeRF | scripts/viewer/run_viewer.py |
| [ns-eval](ns_eval) | Run evaluation metrics for your Model | scripts/eval.py |
| [ns-render](ns_render) | Render out a video of your NeRF | scripts/render.py |
| [ns-export](ns_export) | Export a NeRF into other formats | scripts/exporter.py |
```{toctree}
:maxdepth: 1
:hidden:
ns_install_cli
ns_process_data
ns_download_data
ns_train

9
docs/reference/cli/ns_install_cli.md Normal file
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@ -0,0 +1,9 @@
# ns-install-cli
```{eval-rst}
.. argparse::
:module: scripts.completions.install
:func: get_parser_fn
:prog: ns-install-cli
:nodefault:
```

3
scripts/completions/install.py
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@ -399,3 +399,6 @@ def entrypoint():
if __name__ == "__main__":
entrypoint()
# For sphinx docs
get_parser_fn = lambda: tyro.extras.get_parser(main) # noqa