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Cool, but not touching this; no license and requires Inria's proprietary rasterizer.

People should stop basing all of this new research on proprietary software, when we have open source implementations [1][2].

[1] gsplat: https://github.com/nerfstudio-project/gsplat [2] opensplat: https://github.com/pierotofy/opensplat



It has a license now fwiw.

It's a pretty basic "free for non-commercial use, contact us for commercial use" license.

And the Inria rasterizer is not proprietary either. It's non-commercial open source with the option to purchase a commercial license.

These are perfectly reasonable tech stacks for research projects to build off of. If you have an issue with the license, implement it yourself based on the papers (which all outline the necessary details to do so).


I'm surprised anything in 3D Gaussian splatting uses a rasterizer. I thought those were only used for polygonal data.


Rasterization is actually why 3D Gaussian Splats have been so successful. Being able to render 3DGS scenes by iterating over the objects and drawing the pixels each one covers is much faster than ray-marching every pixel, which is how neural radiance fields (the last hot 3D reconstruction technology) are rendered.


I mean technically rasterization means taking any vector data and plotting it in a 2D space... so I guess it is correct.

But yes, I know what you are getting at. This would normally be done via a software/shader pipeline rather than a GPU's polygonal process.




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