UniqAI/gaussian-splatting
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@ -6,6 +6,8 @@ Bernhard Kerbl*, Georgios Kopanas*, Thomas Leimkühler, George Drettakis (* indi
| [T&T+DB COLMAP (650MB)](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/input/tandt_db.zip) | [Pre-trained Models (14 GB)](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/pretrained/models.zip) | [Viewers for Windows (60MB)](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/binaries/viewers.zip) | [Evaluation Images (7 GB)](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/evaluation/images.zip) | <br>
![Teaser image](assets/teaser.png)
Alternatively, you may access the cloned data (status: August 2023!) from [HuggingFace](https://huggingface.co/camenduru/gaussian-splatting)
This repository contains the official authors implementation associated with the paper "3D Gaussian Splatting for Real-Time Radiance Field Rendering", which can be found [here](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/). We further provide the reference images used to create the error metrics reported in the paper, as well as recently created, pre-trained models.
<a href="https://www.inria.fr/"><img height="100" src="assets/logo_inria.png"> </a>
@ -79,7 +81,7 @@ The optimizer uses PyTorch and CUDA extensions in a Python environment to produc
#### Colab
User [camenduru](https://github.com/camenduru) was kind enough to provide a Colab template that uses pre-built wheels for quick and easy access to the method. Please check it out [here](https://github.com/camenduru/gaussian-splatting-colab).
User [camenduru](https://github.com/camenduru) was kind enough to provide a Colab template that uses this repo's source (status: August 2023!) for quick and easy access to the method. Please check it out [here](https://github.com/camenduru/gaussian-splatting-colab).
#### Local Setup