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Splatw doc updates (#3311)

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* splatw doc

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---------

Co-authored-by: Justin Kerr <justin.g.kerr@gmail.com>
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docs/nerfology/methods/splatw.md
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@ -1,9 +1,10 @@
# Splatfacto in the Wild
This is the implementation of [Splatfacto in the Wild: A Nerfstudio Implementation of Gaussian Splatting for Unconstrained Photo Collections](https://kevinxu02.github.io/gsw.github.io/). The official code can be found [here](https://github.com/KevinXu02/splatfacto-w).
This is the implementation of [Splatfacto in the Wild: A Nerfstudio Implementation of Gaussian Splatting for Unconstrained Photo Collections](https://kevinxu02.github.io/splatfactow). The official code can be found [here](https://github.com/KevinXu02/splatfacto-w).
<video id="teaser" muted autoplay playsinline loop controls width="100%">
<source id="mp4" src="https://github.com/KevinXu02/gsw.github.io/blob/main/static/videos/interp_fountain2.mp4" type="video/mp4">
<source id="mp4" src="https://github.com/KevinXu02/splatfactow/blob/main/static/videos/interp_fountain2.mp4" type="video/mp4">
</video>
## Installation
@ -28,14 +29,23 @@ ns-download-data phototourism --capture-name <capture_name>
```
## Running Splafacto-w
To train with it, download the train/test tsv file from the bottom of [nerf-w](https://nerf-w.github.io/) and put it under the data folder (or copy them from `.\splatfacto-w\dataset_split`). For instance, for Brandenburg Gate the path would be `splatfacto-w\data\brandenburg_gate\brandenburg.tsv`.
To train with it, download the train/test tsv file from the bottom of [nerf-w](https://nerf-w.github.io/) and put it under the data folder (or copy them from `./splatfacto-w/dataset_split`). For instance, for Brandenburg Gate the path would be `your-data-folder/brandenburg_gate/brandenburg.tsv`. You should have the following structure in your data folder:
```
|---brandenburg_gate
| |---dense
| | |---images
| | |---sparse
| | |---stereo
| |---brandenburg.tsv
```
Then, run the command:
```
ns-train splatfacto-w --data [PATH]
```
If you want to train datasets without nerf-w's train/test split or your own datasets, you can run the following command:
If you want to train datasets without nerf-w's train/test split or your own datasets, we provided a light-weight version of the method for general cases. To train with it, you can run the following command
```
ns-train splatfacto-w --data [PATH] [dataparser]
ns-train splatfacto-w-light --data [PATH] [dataparser]
```
For phototourism, the `dataparser` should be `colmap` and you need to change the colmap path through the CLI because phototourism dataparser does not load 3D points.