Comments (5)
This method can give some help. But itβs a little computation heavy.
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Interesting, it's similar to this one from PDAL: https://pdal.io/en/2.7.0/stages/filters.outlier.html#statistical-method (which can be made to run fast).
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Good idea. Open3D also has a few similar built-in functions: https://www.open3d.org/docs/latest/tutorial/Advanced/pointcloud_outlier_removal.html
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I've tried to implement this method in https://github.com/pierotofy/OpenSplat/tree/filter, but although there's some reduction of noise, there's also an increase in other parts (at least in the train dataset) near the object of interest. The validation loss also seems to increase.
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Perhaps removing floaters could be considered as a post-processing step after the 3DGS model gets fully trained. Additionally, I found enhancing the training code, improving camera pose, and removing blurry image could substantially minimize floating artifacts.
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Related Issues (20)
- Docker build on a100 gpu libtorch cuda error HOT 4
- how to quantitatively evaluate the training results?
- How to generate image in new views?
- The size of the data obtained by training with OpenSplat is half the size of the Python version
- Cannot view generated .ply image HOT 5
- Integrate the new Reduced 3DGS paper
- depth rendering and optimization
- what is this error?
- Issue building for MacOS HOT 4
- Docker build taking forever on CUDA step HOT 4
- Build Failed HOT 9
- Infer the other cameras
- Integrate Markov Chain Monte Carlo paper improvements HOT 1
- Use the lastest version of gsplat cuda backend HOT 1
- Windows Installation CUDA Errors HOT 4
- Build Failed on Ubuntu
- Make failed in Windows no makefile found HOT 2
- Better Documentation on GSplat Version and Porting Process HOT 1
- No CUDA toolset found error
- Custom dataset issues
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