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Help for dynamic multiview custom dataset #124

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chanwo0kim opened this issue Apr 23, 2024 · 5 comments
Open

Help for dynamic multiview custom dataset #124

chanwo0kim opened this issue Apr 23, 2024 · 5 comments

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@chanwo0kim
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chanwo0kim commented Apr 23, 2024

Hello!

I am very impressed with this research.
So I want to run this code on my own dynamic, multiview dataset.

However, I am having a hard time understanding how to set up the dataset.

I used bash multipleviewprogress.sh (youe dataset name) and made the folder as

image

After that, how should I do that?
When I use python train.py ~~ then the error "Could not recognize scene type" is raised.

I think the problem is probably because there is no "sparse" folder or "train_meta.json".

So, I used ns-process-data images --data data/your-data --output-dir data/your-ns-data for first frames of each view.
However, although Colmap worked well in Colmap GUI or multipleviewprogress.sh, only two camera poses were matched in ns-process-data.

I will wait for your reply. Thank you!

@guanjunwu
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hi, is there a file named points3D_multipleview.ply in your folder?
If exists, the format of the dataset will be recognized correctly.

@chanwo0kim
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chanwo0kim commented Apr 23, 2024

Hi, thanks for your reply.

Yes. I already have points3D_multipleview.ply in my folder like this.
image

Then, can you tell me what to do next for training?

Sorry for taking your time

@guanjunwu
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Hi, it seems it will work.
Just change your folder name and put in "data/multipleview/mydataset"
Then add a config files "arguments/multipleview/mydataset.py"
and run
python train.py -s data/multipleview/mydataset --port 6017 --expname "multipleview/mydataset" --configs arguments/multipleview/mydataset.py

@chanwo0kim
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Actually, even though I did that, but the error "Could not recognize scene type!" occurred.

So, I think another execution manner would be to set the data in dynerf format and run it.

Thank you for answer.

@chanwo0kim
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hello! I opened the issue because I have one more question.

I confirmed that the coarse stage is coded to sample uid randomly.

However, from what I understand through the paper, I understand that coarse 3D-GS is optimized on the first frame in the coarse stage, and the deformation field is learned in the fine stage for other time axis.

If I learn in the same way as the code, the 3D Gaussian from the coarse stage will be a 3D Gaussian for the global scene structure, not a 3D Gaussian for a certain frame.

Then, when a motion that significantly changes the global structure is inputted (e.g., the scene itself changes), it is expected that it will not converge properly. Is this correct?

@chanwo0kim chanwo0kim reopened this May 9, 2024
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