UniqAI/gaussian-splatting
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Undo spatial lr removal

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bkerbl 2023-07-12 12:14:22 +02:00
parent b7031f116f
commit 6f1e3ec83a
3 changed files with 3 additions and 5 deletions
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2
SIBR_viewers

@ -1 +1 @@
Subproject commit 549b0c2d2c82e186cd5d705b11c53ff5e27b14e5 Subproject commit 29dd2f3a5dc866664b8e0f04bce34dc81e5e6088

4
full_eval.py
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@ -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
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@ -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()