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@kwea123
kwea123 authored May 1, 2020
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Expand Up @@ -9,6 +9,7 @@ Reference pytorch implementation: [nerf-pytorch](https://github.com/yenchenlin/n

* Multi-gpu training: Training on 8 GPUs finishes within 1 hour for the synthetic dataset!
* [Colab](#colab) notebooks to allow easy usage!
* Reconstruct **colored** mesh!

# Installation

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```
It will create folder `results/{dataset_name}/{scene_name}` and run inference on all test data, finally create a gif out of them.

Example of lego scene using pretrained model, shown at 1/2 scale: (PSNR=31.39, paper=32.54)
Example of lego scene using pretrained model and the reconstructed **colored** mesh: (PSNR=31.39, paper=32.54)

![lego](https://user-images.githubusercontent.com/11364490/79932648-f8a1e680-8488-11ea-98fe-c11ec22fc8a1.gif)
<p>
<img src="https://user-images.githubusercontent.com/11364490/79932648-f8a1e680-8488-11ea-98fe-c11ec22fc8a1.gif" width="200">
<img src="https://user-images.githubusercontent.com/11364490/80813179-822d8300-8c04-11ea-84e6-142f04714c58.png" width="200">
</p>

Example of fern scene using pretrained model, shown at 1/2 scale:
Example of fern scene using pretrained model:

![fern](https://user-images.githubusercontent.com/11364490/79932650-f9d31380-8488-11ea-8dad-b70a6a3daa6e.gif)

Example of own scene ([Silica GGO figure](https://www.youtube.com/watch?v=hVQIvEq_Av0)). Click to link to youtube video.
Example of own scene ([Silica GGO figure](https://www.youtube.com/watch?v=hVQIvEq_Av0)) and the reconstructed **colored** mesh. Click to link to youtube video.

[![silica](https://user-images.githubusercontent.com/11364490/80279695-324d4880-873a-11ea-961a-d6350e149ece.gif)]( https://youtu.be/yH1ZBcdNsUY)
<p>
<a href="https://youtu.be/yH1ZBcdNsUY">
<img src="https://user-images.githubusercontent.com/11364490/80279695-324d4880-873a-11ea-961a-d6350e149ece.gif" height="252">
</a>
<img src="https://user-images.githubusercontent.com/11364490/80813184-83f74680-8c04-11ea-8606-40580f753355.png" height="252">
</p>

# Notes on differences with the original repo

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