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junhua-l avatar junhua-l commented on August 15, 2024

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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guanjunwu avatar guanjunwu commented on August 15, 2024
  1. 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])
  2. you will find results in output/your_checkpoint_dir

P.S: I visualized results in Blender(Open3D also works)

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junhua-l avatar junhua-l commented on August 15, 2024

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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junhua-l avatar junhua-l commented on August 15, 2024

I have modified a version myself and it has worked now. Thank you for your answer : )

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henrypearce4D avatar henrypearce4D commented on August 15, 2024

@JunhuaLiu0 could you share your modification?

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imjxy avatar imjxy commented on August 15, 2024

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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