diff --git a/docs/tutorials/data/creating_dataset.md b/docs/tutorials/data/creating_dataset.md
index a77116c76..89719f92c 100644
--- a/docs/tutorials/data/creating_dataset.md
+++ b/docs/tutorials/data/creating_dataset.md
@@ -2,47 +2,67 @@
 
 Training model on existing datasets is only so fun. If you would like to train on self captured data you will need to process the data into an existing format. Specifically we need to know the camera poses for each image. [COLMAP](https://github.com/colmap/colmap) is a standard tool for extracting poses. It is possible to use other methods like [SLAM](https://en.wikipedia.org/wiki/Simultaneous_localization_and_mapping) or hardware recorded poses. We intend to add documentation for these other methods in the future.
 
-#### Recover poses using COLMAP
+## Nerfactory dataset
 
-:::{admonition} Notes
-:class: warning
+To assist running on custom data we have a script that will process a video or folder of images into a format that is compatible with nerfactory. We use [COLMAP](https://colmap.github.io) and [FFmpeg](https://ffmpeg.org/download.html) in our data processing script, please have these installed. We have provided a quickstart to installing COLMAP below, FFmpeg can be downloaded from [here](https://ffmpeg.org/download.html)
 
-- This method assumes the images were captured with a standard _pinhole-like_ camera. 360° captures will not work.
-- COLMAP is notoriously finicky. Try your best to capture overlapping, non-blurry images.
-  :::
-
-These instructions will recover poses for your own images using [COLMAP](https://github.com/colmap/colmap) and save the data in the `instant_ngp` format.
-
-Please download [this file](https://github.com/NVlabs/instant-ngp/blob/07d8e2ca7232f97397ab73af9b56c7db639d3445/scripts/colmap2nerf.py) and place it at `scripts/colmap2nerf.py` courtesy of the [Instant-NGP](https://github.com/NVlabs/instant-ngp) authors. This script will use COLMAP to process images of a video file into a reconstruction with camera poses. You can see their write-up [here](https://github.com/NVlabs/instant-ngp/blob/master/docs/nerf_dataset_tips.md) for instructions.
-
-For example, maybe we have a video named `bear.mp4`. We can use the commands described in the Instant-NGP repo to create our dataset. We first create a a data folder. Then, we navigate to this folder. Finally, we run the colmap2nerf.py script with a specified `video_fps` and `aabb_scale`. The recommended number of frames is around 50-150 and the `aabb_scale` is the extent of the scene. The origin is the center of the aabb and `aabb_scale` is the length of each side of the box.
+To process your data run:
 
 ```bash
-# Navigate to the repo.
-cd /path/to/nerfactory
-
-# Setup the environment variables.
-nerfactory_DIR=`pwd`
-DATASET_FORMAT=instant_ngp
-DATASET_NAME=bear
-
-# Create and navigate to the data folder.
-mkdir data/$DATASET_FORMAT/$DATASET_NAME
-cd data/$DATASET_FORMAT/$DATASET_NAME
-
-# Run the COLMAP script.
-python $nerfactory_DIR/scripts/colmap2nerf.py \
-    --video_in /path/to/bear.mp4 \
-    --video_fps 2 \
-    --run_colmap \
-    --aabb_scale 16
+python scripts/process_data.py --data FOLDER_OR_VIDEO --output-dir PROCESSED_DATA_DIR
 ```
 
-In the config set:
+If your data was captured with a fisheye camera add `--camera-type fisheye`.
 
-```yaml
+:::{admonition} Tip
+:class: info
 
----
-data_directory: data/instant_ngp/bear
-dataset_format: instant_ngp
+- COLMAP can be finicky. Try your best to capture overlapping, non-blurry images.
+  :::
+
+### Training on your data
+
+Simply specify that you are using the `nerfactory` dataparser and point the data directory to your processed data.
+
+```bash
+python scripts/run_train.py instant-ngp nerfactory --pipeline.datamanager.dataparser.data-directory PROCESSED_DATA_DIR
 ```
+
+### Installing COLMAP
+
+The COLMAP maintainers recommend using VCPKG to install COLAMP. If the following command do not work, please refer to the [COLMAP installation guide](https://colmap.github.io/install.html) for additional installation methods.
+
+::::{tab-set}
+:::{tab-item} Linux
+
+```bash
+git clone https://github.com/microsoft/vcpkg
+cd vcpkg
+./bootstrap-vcpkg.sh
+./vcpkg install colmap[cuda]:x64-linux
+```
+
+:::
+
+:::{tab-item} OSX
+
+```bash
+git clone https://github.com/microsoft/vcpkg
+cd vcpkg
+./bootstrap-vcpkg.sh
+./vcpkg install colmap[cuda]:x64-osx
+```
+
+:::
+
+:::{tab-item} Windows
+
+```bash
+git clone https://github.com/microsoft/vcpkg
+cd vcpkg
+./bootstrap-vcpkg.sh
+./vcpkg install colmap[cuda]:x64-windows
+```
+
+:::
+::::