UniqAI/gaussian-splatting
1
0
Fork 0
mirror of https://github.com/graphdeco-inria/gaussian-splatting synced 2024-11-22 08:18:17 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

Undo spatial lr removal

Browse Source
This commit is contained in:
bkerbl 2023-07-12 12:14:22 +02:00
parent 770f6b05e2
commit 2d2d5ce1c3
2 changed files with 2 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
full_eval.py
View File

@ -12,7 +12,7 @@
import os import os
from argparse import ArgumentParser 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"] mipnerf360_indoor_scenes = ["room", "counter", "kitchen", "bonsai"]
tanks_and_temples_scenes = ["truck", "train"] tanks_and_temples_scenes = ["truck", "train"]
deep_blending_scenes = ["drjohnson", "playroom"] deep_blending_scenes = ["drjohnson", "playroom"]
@ -37,7 +37,7 @@ if not args.skip_training or not args.skip_rendering:
args = parser.parse_args() args = parser.parse_args()
if not args.skip_training: if not args.skip_training:
common_args = " --eval --save_iterations -1" common_args = " --quiet --eval --test_iterations -1"
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)

2
scene/gaussian_model.py
View File

@ -84,9 +84,7 @@ class GaussianModel:
self.active_sh_degree += 1 self.active_sh_degree += 1
def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float): def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float):
spatial_lr_scale = 5
self.spatial_lr_scale = spatial_lr_scale self.spatial_lr_scale = spatial_lr_scale
#print(spatial_lr_scale)
fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda() fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda()
fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).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() features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda()