Comments (3)
There are a lot of things, but most of them are related to preparing a good dataset for photogrammetry.
Colmap and GS take (literally) everything you give them, so weak images too.
If you are thinking about taking photos indoors - this is a very good tutorial.
https://radiancefields.com/gaussian-splatting-brings-art-exhibitions-online-with-yulei
https://medium.com/@heyulei/capture-images-for-gaussian-splatting-81d081bbc826
For me most important thing is sharp pictures with very deep depth of field
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Sharp pictures...
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Are there any tips on how to get rid of floating noise(attached example image) and to make everything more consistent? By consistency I mean if there are gaussians belonging to chair(or any other object), then it is usual case that when looking from some angles the surface of object is not smooth.
I believe that depth supervision and more images could help. Is there anything else
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Related Issues (20)
- About Visualization Results HOT 1
- Error with gradients when using camera with arbitrary principal points outside of Image Boundaries
- About visualiazation with SIBR viewers. How does it generate the 3d model. What files are important in the process. HOT 2
- About the J matrix and projection matrix
- Failed building wheels for submodules/diff-gaussian-rasterization (WSL2) HOT 1
- Changed gpu, now training slow(er) HOT 1
- Problems with converty.py (maybe?) HOT 1
- Failed pip install submodules\diff-gaussian-rasterization HOT 1
- RuntimeError: min(): Expected reduction dim to be specified for input.numel() == 0.
- Computing Gaussians Directions
- rendering with custom cameras Matrix
- Slender black Gaussian
- Question about obtaining exponential falloff multiplied to alpha HOT 1
- No module named simple_knn HOT 5
- conert.py crash HOT 1
- viewers app problem, beg for help! HOT 2
- Backwards rendering / invert raasterization process
- Issue with Running convert.py Script in VSCode Terminal HOT 1
- Grayscale (or 1-channel image)
- What is the use of function `segment` in class Gaussianmodel in <gaussian_model.py>?
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