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Still, NVIDIA's achievement (and Thomas Müller in particular) is amazing. Thomas and his collaborators achieved an almost 1000x performance improvement, by a combination of algorithmical and implementation tricks.
I highly recommend trying this at home:
https://nvlabs.github.io/instant-ngp/
https://github.com/NVlabs/instant-ngp
Very straightforward and gives better insight into what NeRF is than any shiny marketing demo.
This is great, and the paper+codebase they're referring to (but not linking, here [1]) is neat too.
The research is moving fast though, so if you want something almost as fast without specialized CUDA kernels (just plain pytorch) you're in luck: https://github.com/apchenstu/TensoRF
As a bonus you also get a more compact representation of the scene.
[1] https://github.com/NVlabs/instant-ngp
There is an explosion of NeRF papers:
https://github.com/yenchenlin/awesome-NeRF
It's possible to capture video / movement to into NeRFs, possible to animate, relight, compose multiple NeRF scenes, and a lot of papers are about making faster more efficient and higher quality NeRF. Looks very promising.
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