Splatfacto in the Wild

This is the implementation of Splatfacto in the Wild: A Nerfstudio Implementation of Gaussian Splatting for Unconstrained Photo Collections. The official code can be found here.

Installation

This repository follows the nerfstudio method template

1. Install Nerfstudio dependencies

Please follow the Nerfstudio installation guide to create an environment and install dependencies.

2. Install the repository

Run the following commands: pip install git+https://github.com/KevinXu02/splatfacto-w

Then, run ns-install-cli.

3. Check installation

Run ns-train splatfacto-w --help. You should see the help message for the splatfacto-w method.

Downloading data

You can download the phototourism dataset from running.

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 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, 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-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.