From d29178568e8d4eebbf1da40197ce3c34827fe7e9 Mon Sep 17 00:00:00 2001
From: bkerbl <bkerbl@ad.inria.fr>
Date: Mon, 10 Jul 2023 20:10:42 +0200
Subject: [PATCH] Better readme

---
 README.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index ebbe3d2..7ec2b7f 100644
--- a/README.md
+++ b/README.md
@@ -92,7 +92,7 @@ conda env create --file environment.yml --prefix <Drive>/<env_path>/gaussian_spl
 conda activate <Drive>/<env_path>/gaussian_splatting
 ```
 
-If you can afford the disk space, we recommend using our environment files for setting up a training environment identical to ours. If you want to make changes, please note that major version changes might affect the results of our method. However, our (limited) experiments suggest that the codebase works just fine inside a more up-to-date environment (Python 3.8, PyTorch 2.0.0, CUDA 11.8).
+If you can afford the disk space, we recommend using our environment files for setting up a training environment identical to ours. If you want to make modifications, please note that major version changes might affect the results of our method. However, our (limited) experiments suggest that the codebase works just fine inside a more up-to-date environment (Python 3.8, PyTorch 2.0.0, CUDA 11.8).
 
 ### Running
 
@@ -181,7 +181,7 @@ python render.py -m <path to trained model> # Generate renderings
 python metrics.py -m <path to trained model> # Compute error metrics on renderings
 ```
 
-If you want to evaluate our pre-trained models, you will have to download the corresponding source data sets and indicate their location to ```render.py``` with an additional ```--source_path/-s``` flag. Note: The pre-trained models were created with the release codebase. This code base has been cleaned up and includes bugfixes, hence the metrics you get from evaluating them will differ from those in the paper.
+If you want to evaluate our [pre-trained models](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/pretrained/models.zip), you will have to download the corresponding source data sets and indicate their location to ```render.py``` with an additional ```--source_path/-s``` flag. Note: The pre-trained models were created with the release codebase. This code base has been cleaned up and includes bugfixes, hence the metrics you get from evaluating them will differ from those in the paper.
 ```shell
 python render.py -m <path to pre-trained model> -s <path to COLMAP dataset>
 python metrics.py -m <path to pre-trained model>
@@ -400,6 +400,7 @@ If you have your own COLMAP dataset without undistortion (e.g., using ```OPENCV`
 |---distorted
 |   |---sparse
 |   |   |---0
+|   |   |   |---...
 |   |---database.db
 ```
 Then run