Update

Author: bkerbl

README.md

@@ -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) |
 [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)
 
 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. 

full_eval.py

@@ -38,18 +38,18 @@
 
 if not args.skip_training:
     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:
         source = args.mipnerf360 + "/" + scene
         os.system("python train.py -s " + source + " -i images_4 -m " + args.output_path + "/" + scene + common_args)
     for scene in mipnerf360_indoor_scenes:
         source = args.mipnerf360 + "/" + scene
         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:
     all_sources = []