Splatw doc updates

docs/nerfology/methods/splatw.md

@@ -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.