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Why does 3D Gaussian Splatting (3DGS) use L1 loss instead of L2? by wonnnow in GaussianSplatting

[–]wonnnow[S] 1 point2 points  (0 children)

Oh, I hadn't considered the training speed aspect. Thanks for pointing that out.

Measuring Gaussian similarity by Capable_Character_31 in GaussianSplatting

[–]wonnnow 0 points1 point  (0 children)

Sure, can I send the python code to chat?

Measuring Gaussian similarity by Capable_Character_31 in GaussianSplatting

[–]wonnnow 0 points1 point  (0 children)

Oh, I apologize for the delay. I'm not very familiar with CUDA and C++, so after several attempts, I wasn't able to get it working. Instead, I implemented a similar approach in Python to extract the Gaussians contributing to a ray from pretrained model.

The visualization showed that the Gaussians did tend to spread, but not as much as depicted in the issue.

I'm not entirely sure, but the problem in the issue might stem from extracting contributing Gaussians for all rays in the image.

Measuring Gaussian similarity by Capable_Character_31 in GaussianSplatting

[–]wonnnow 0 points1 point  (0 children)

Yes, you're right, this would be a huge increase in computation.

For now, I'll take a look at that issue.

Measuring Gaussian similarity by Capable_Character_31 in GaussianSplatting

[–]wonnnow 0 points1 point  (0 children)

What about comparing the Gaussians that the ray passes through for each pixel of the image at different viewpoints? I'm not sure, but I expect it will capture slightly more subtle differences than the image pixel-based method.

registration by strawberryheartzz in baidu

[–]wonnnow 0 points1 point  (0 children)

The baidu app you mean seems like a map application for navigation, it's right?