UniqAI/gaussian-splatting
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bkerbl 2023-07-09 15:14:31 +02:00
parent 424012cd0d
commit 8943f57b26
2 changed files with 7 additions and 7 deletions
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2
README.md
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@ -3,7 +3,7 @@ Bernhard Kerbl*, Georgios Kopanas*, Thomas Leimkühler, George Drettakis (* indi
| [Webpage](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/) | [Full Paper](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/3d_gaussian_splatting_high.pdf) | | [Webpage](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/) | [Full Paper](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/3d_gaussian_splatting_high.pdf) |
[Video](https://youtu.be/T_kXY43VZnk) | [Other GRAPHDECO Publications](http://www-sop.inria.fr/reves/publis/gdindex.php) | [FUNGRAPH project page](https://fungraph.inria.fr) [Video](https://youtu.be/T_kXY43VZnk) | [Other GRAPHDECO Publications](http://www-sop.inria.fr/reves/publis/gdindex.php) | [FUNGRAPH project page](https://fungraph.inria.fr)
[T&T+DB Datasets (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) | [Viewer Binaries for Windows (60MB)](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/binaries/viewers.zip) | [Evaluation Images](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/evaluation/images.zip) | <br> [T&T+DB Datasets (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) | [Viewer Binaries 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) ![Teaser image](assets/teaser.png)
This repository contains the code 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. This repository contains the code 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.

12
full_eval.py
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@ -38,18 +38,18 @@ if not args.skip_training or not args.skip_rendering:
if not args.skip_training: if not args.skip_training:
common_args = " --quiet --eval --test_iterations -1" common_args = " --quiet --eval --test_iterations -1"
for scene in tanks_and_temples_scenes:
source = args.tanksandtemples + "/" + scene
os.system("python train.py -s " + source + " -m " + args.output_path + "/" + scene + common_args)
for scene in deep_blending_scenes:
source = args.deepblending + "/" + scene
os.system("python train.py -s " + source + " -m " + args.output_path + "/" + scene + common_args)
for scene in mipnerf360_outdoor_scenes: for scene in mipnerf360_outdoor_scenes:
source = args.mipnerf360 + "/" + scene source = args.mipnerf360 + "/" + scene
os.system("python train.py -s " + source + " -i images_4 -m " + args.output_path + "/" + scene + common_args) os.system("python train.py -s " + source + " -i images_4 -m " + args.output_path + "/" + scene + common_args)
for scene in mipnerf360_indoor_scenes: for scene in mipnerf360_indoor_scenes:
source = args.mipnerf360 + "/" + scene source = args.mipnerf360 + "/" + scene
os.system("python train.py -s " + source + " -i images_2 -m " + args.output_path + "/" + scene + common_args) os.system("python train.py -s " + source + " -i images_2 -m " + args.output_path + "/" + scene + common_args)
for scene in tanks_and_temples_scenes:
source = args.tanksandtemples + "/" + scene
os.system("python train.py -s " + source + " -m " + args.output_path + "/" + scene + common_args)
for scene in deep_blending_scenes:
source = args.deepblending + "/" + scene
os.system("python train.py -s " + source + " -m " + args.output_path + "/" + scene + common_args)
if not args.skip_rendering: if not args.skip_rendering:
all_sources = [] all_sources = []