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Congrong XuandJustin Kerr
d2510aa02f
* splatw doc * fix * fix typo * update doc --------- Co-authored-by: Justin Kerr <justin.g.kerr@gmail.com>
51 lines
2.2 KiB
Markdown
51 lines
2.2 KiB
Markdown
# Splatfacto in the Wild
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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).
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<video id="teaser" muted autoplay playsinline loop controls width="100%">
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<source id="mp4" src="https://github.com/KevinXu02/splatfactow/blob/main/static/videos/interp_fountain2.mp4" type="video/mp4">
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</video>
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## Installation
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This repository follows the nerfstudio method [template](https://github.com/nerfstudio-project/nerfstudio-method-template/tree/main)
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### 1. Install Nerfstudio dependencies
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Please follow the Nerfstudio [installation guide](https://docs.nerf.studio/quickstart/installation.html) to create an environment and install dependencies.
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### 2. Install the repository
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Run the following commands:
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`pip install git+https://github.com/KevinXu02/splatfacto-w`
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Then, run `ns-install-cli`.
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### 3. Check installation
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Run `ns-train splatfacto-w --help`. You should see the help message for the splatfacto-w method.
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## Downloading data
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You can download the phototourism dataset from running.
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```
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ns-download-data phototourism --capture-name <capture_name>
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```
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## Running Splafacto-w
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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:
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```
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|---brandenburg_gate
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| |---dense
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| | |---images
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| | |---sparse
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| | |---stereo
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| |---brandenburg.tsv
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```
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Then, run the command:
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```
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ns-train splatfacto-w --data [PATH]
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```
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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
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```
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ns-train splatfacto-w-light --data [PATH] [dataparser]
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```
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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. |