From d4e1e922bdf3806cd57beacfb68be795cf3ebdac Mon Sep 17 00:00:00 2001
From: bkerbl <bkerbl@ad.inria.fr>
Date: Wed, 12 Jul 2023 12:14:22 +0200
Subject: [PATCH] Undo spatial lr removal

---
 SIBR_viewers            | 2 +-
 full_eval.py            | 4 ++--
 scene/gaussian_model.py | 2 --
 3 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/SIBR_viewers b/SIBR_viewers
index 549b0c2..29dd2f3 160000
--- a/SIBR_viewers
+++ b/SIBR_viewers
@@ -1 +1 @@
-Subproject commit 549b0c2d2c82e186cd5d705b11c53ff5e27b14e5
+Subproject commit 29dd2f3a5dc866664b8e0f04bce34dc81e5e6088
diff --git a/full_eval.py b/full_eval.py
index 672ee12..d174ef8 100644
--- a/full_eval.py
+++ b/full_eval.py
@@ -12,7 +12,7 @@
 import os
 from argparse import ArgumentParser
 
-mipnerf360_outdoor_scenes = ["flowers", "garden", "stump", "treehill"]
+mipnerf360_outdoor_scenes = ["bicycle", "flowers", "garden", "stump", "treehill"]
 mipnerf360_indoor_scenes = ["room", "counter", "kitchen", "bonsai"]
 tanks_and_temples_scenes = ["truck", "train"]
 deep_blending_scenes = ["drjohnson", "playroom"]
@@ -37,7 +37,7 @@ if not args.skip_training or not args.skip_rendering:
     args = parser.parse_args()
 
 if not args.skip_training:
-    common_args = "  --eval --save_iterations -1"
+    common_args = " --quiet --eval --test_iterations -1"
     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)
diff --git a/scene/gaussian_model.py b/scene/gaussian_model.py
index f50a4b2..20a81d4 100644
--- a/scene/gaussian_model.py
+++ b/scene/gaussian_model.py
@@ -84,9 +84,7 @@ class GaussianModel:
             self.active_sh_degree += 1
 
     def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float):
-        spatial_lr_scale = 5
         self.spatial_lr_scale = spatial_lr_scale
-        #print(spatial_lr_scale)
         fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda()
         fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda())
         features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda()