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Windows Implement, Success! #182
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4. Add: For my own dataset1.1 prepare dataset (pics)1.2 run the colmap script (contained in convert.py) and train.py for gaussian_splatiingjust run then just run the same scripts like before: 1.3 result |
I was able to successfully use this on Windows because of this post! thank you so much :) |
after following your guides I was able to extract a mesh and textures... but like your example images, the quality of the mesh is very low. Do you know of any way to improve mesh quality? The images in the SuGaR repo look amazing, but mine don't look like it at all... |
Hello, I'm having trouble deploying the environment using Windows. It seems that simply using |
in the installation instructions it also explains how to install all the dependencies if that command fails. You have to do it that way |
To be honest, I don't know exactly how to get the high-quality mesh. /cry I'm studying hard to learn about the project. I hope somedays later I could give you some advice about it. |
You could install the packages seperately. In addition, there are some problems I encountered during the installation, maybe helpful.
|
Thank you for your help. I did encounter an issue with installing the package |
You are right, I didn't notice that. Thank you for your help. I have successfully deployed the environment. |
Is there a way to preceed with digital video. Not recorded on video cam. I creating Gaussian Splats in Lucid Dreamer for backgrounds and searching a way to add mesh. So I don't have COLMAP dataset only .ply or record from viewer |
Hi,I have a result from 3dgaussaian splatting,include iteration_7000 and iteration_30000,what can i do to extract a mesh from ply? |
1. gaussian_splatting
1.1 run train.py (gaussian_splatting):
This should be easy:
run:
python gaussian_splatting/train.py -s <path to COLMAP dataset> --iterations 7000 -m <path to the desired output directory>
my for example:
python gaussian_splatting/train.py -s gaussian_splatting\sherioc_dataset\train --iterations 7000 -m gaussian_splatting\sherioc_output\train\
1.2 output example
sherioc_dataset
is input foldersherioc_dataset
is output folder2. sugar
2.1 change all the path that for Windows(change all the '/' to '\')
2.2 train.py contains four parts:
you can choose run whole script-Ⅰ or run seperately-Ⅱ
Ⅰ. run whole script (train.py)
python train.py -s gaussian_splatting\sherioc_dataset\train -c gaussian_splatting\sherioc_output\train\ -r "density"
Ⅱ. run seperately
suppose you choose
![image](https://private-user-images.githubusercontent.com/130974708/319582828-803323fc-62e4-472c-8f21-8cc441f1ee80.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MTgzMjkxMzMsIm5iZiI6MTcxODMyODgzMywicGF0aCI6Ii8xMzA5NzQ3MDgvMzE5NTgyODI4LTgwMzMyM2ZjLTYyZTQtNDcyYy04ZjIxLThjYzQ0MWYxZWU4MC5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwNjE0JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDYxNFQwMTMzNTNaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1iZTJlNzI3OWRhN2FkNWE0MWQwMzAzNjlhYmI2ZjdjOWZhYWQ4Njg2NWM4ZDZiNDU2Mjc0MzQxNDQ4OGQ1NjY2JlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.bKssU3MCaAEEzRvEVh90vK1Zw11KS4lOYJm3zsRgc0o)
"density"
like mepython train_coarse_density.py -s gaussian_splatting\sherioc_dataset\train -c gaussian_splatting\sherioc_output\train\
python extract_mesh.py -s gaussian_splatting\sherioc_dataset\train -c gaussian_splatting\sherioc_output\train\ -m output\coarse\train\sugarcoarse_3Dgs7000_densityestim02_sdfnorm02\15000.pt
then run
![image](https://private-user-images.githubusercontent.com/130974708/319817951-8c80970b-96c5-43ec-bb6f-246f2f048e76.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MTgzMjkxMzMsIm5iZiI6MTcxODMyODgzMywicGF0aCI6Ii8xMzA5NzQ3MDgvMzE5ODE3OTUxLThjODA5NzBiLTk2YzUtNDNlYy1iYjZmLTI0NmYyZjA0OGU3Ni5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwNjE0JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDYxNFQwMTMzNTNaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT02MGZhMzIzNTMyNGNmOWVkMjcxYmQzMjNmM2NkNzAwODNiNmE5MzhkMjI0YzdjOTM5ZDE2MGEwMDA1ZmJiODgxJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.-ofO0O_-CRu2Lb8NKdYVk5Q_1S8PX5mxBceGEL6E7Nw)
python train_refined.py -s gaussian_splatting\sherioc_dataset\train -c gaussian_splatting\sherioc_output\train\ -m output\coarse_mesh\train\sugarmesh_3Dgs7000_densityestim02_sdfnorm02_level05_decim1000000.ply
python extract_refined_mesh_with_texture.py -s gaussian_splatting\sherioc_dataset\train -c gaussian_splatting\sherioc_output\train\ -m output\refined\train\sugarfine_3Dgs7000_densityestim02_sdfnorm02_level05_decim200000_normalconsistency01_gaussperface1\15000.pt
Finally, you should get a result like this:
3. viewer on Windows
3.1 obj model
you can observe the ply and obj model online or in blender, this needn't the viewer:
![image](https://private-user-images.githubusercontent.com/130974708/320183982-ed06fc0c-aee0-42cb-9c1c-6c342d490f5e.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MTgzMjkxMzMsIm5iZiI6MTcxODMyODgzMywicGF0aCI6Ii8xMzA5NzQ3MDgvMzIwMTgzOTgyLWVkMDZmYzBjLWFlZTAtNDJjYi05YzFjLTZjMzQyZDQ5MGY1ZS5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwNjE0JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDYxNFQwMTMzNTNaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1kZjFiOWJkYTJkNDYwM2YyMDQ0ZmU5ODI0MzA5MzNlYjFjNDg4N2NkN2IzNDUxNGMwYTY5Mzc4ZDdhN2MwNDNlJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.itY9fyVG_N72RVf7kRMis77o64wIKP2p1Fd_FSNLPvg)
this is the rendered mesh, of course there is a problem that there is a large amount of debris.
3.2 viewer
just run
![image](https://private-user-images.githubusercontent.com/130974708/320184176-aee8c8e1-8af6-43be-9392-fb4cfa3f5176.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MTgzMjkxMzMsIm5iZiI6MTcxODMyODgzMywicGF0aCI6Ii8xMzA5NzQ3MDgvMzIwMTg0MTc2LWFlZThjOGUxLThhZjYtNDNiZS05MzkyLWZiNGNmYTNmNTE3Ni5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwNjE0JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDYxNFQwMTMzNTNaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT00N2QzOTYyZTYwMzMwMDhmNWE2ODM1MGU4MDhjZDc3OTI0YzE2OTRmYjA5MmVhYjE3MjdkNzYwOGEwYTM1ODZlJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.3Mgv-p-2kaGYkDCPu9ew22FHSBwKCWu_Zcf3OG1YA1I)
conda install -c conda-forge nodejs
after that, change to the sugar_viewer dictory
cd sugar_viewer
run
npm install
then change to the root dictory
cd ..
then run
python run_viewer.py -p output\refined_ply\train\<ply model name>
Viewer result:
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