Comments (6)
Since Figure 5 in the article also shows the visualizations of point clouds with different training iterations, I guess there are some ways to derive the point cloud.
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- change the argument metioned in "save_iterations" like:
parser.add_argument("--save_iterations", nargs="+", type=int, default=[2000, 3000, 5000, 7_000, 9000, 10000,12000, 14000, 20000, 30_000, 45000, 60000])
- you will find results in
output/your_checkpoint_dir
P.S: I visualized results in Blender(Open3D also works)
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Sorry for disturbing you again. That's not what I'm looking for.
What you provide is to store the static point cloud on different training iterations. Since this is a dynamic Gaussian, what I want is the point cloud of each frame when the dynamic Gaussian is played after training.
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I have modified a version myself and it has worked now. Thank you for your answer : )
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@JunhuaLiu0 could you share your modification?
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I have modified a version myself and it has worked now. Thank you for your answer : )我自己修改了一个版本,现在已经可以运行了。谢谢您的回答 : )
@junhua-l could you share your modification? Or can you provide some hints?
Thank you
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