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https://github.com/graphdeco-inria/gaussian-splatting
synced 2024-11-22 00:08:02 +00:00
Undo spatial lr removal
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@ -12,7 +12,7 @@
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import os
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from argparse import ArgumentParser
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mipnerf360_outdoor_scenes = ["flowers", "garden", "stump", "treehill"]
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mipnerf360_outdoor_scenes = ["bicycle", "flowers", "garden", "stump", "treehill"]
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mipnerf360_indoor_scenes = ["room", "counter", "kitchen", "bonsai"]
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tanks_and_temples_scenes = ["truck", "train"]
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deep_blending_scenes = ["drjohnson", "playroom"]
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@ -37,7 +37,7 @@ if not args.skip_training or not args.skip_rendering:
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args = parser.parse_args()
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if not args.skip_training:
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common_args = " --eval --save_iterations -1"
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common_args = " --quiet --eval --test_iterations -1"
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for scene in mipnerf360_outdoor_scenes:
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source = args.mipnerf360 + "/" + scene
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os.system("python train.py -s " + source + " -i images_4 -m " + args.output_path + "/" + scene + common_args)
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@ -84,9 +84,7 @@ class GaussianModel:
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self.active_sh_degree += 1
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def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float):
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spatial_lr_scale = 5
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self.spatial_lr_scale = spatial_lr_scale
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#print(spatial_lr_scale)
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fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda()
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fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda())
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features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda()
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