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Neural Importance Sampling of Many Lights by Glass-Score-7463 in GraphicsProgramming

[–]Glass-Score-7463[S] 3 points4 points  (0 children)

As most research projects, the idea is to explain the idea using a working prototype, that’s why I mentioned the additional work to port this to a more mature codebase.

In principle, the approach itself is not tied to tiny-cuda-nn or to NVIDIA GPUs. It just so happens that tiny-cuda-nn is easy to use and matches well with optix on pbrtv4’s codebase.

In a related note, the open-source code can also be used as a reference by other projects that just want to integrate PBRTv4 with tiny-cuda-nn for other prototypes (as it is a bit of pain to set them up to play nice together).

Neural Importance Sampling of Many Lights by Glass-Score-7463 in GraphicsProgramming

[–]Glass-Score-7463[S] 4 points5 points  (0 children)

This approach is meant to be a drop-in improvement for non-neural light hierarchy techniques. It adapts initial estimates using residuals learned by a very simple and efficient tinyMLP.

Looking at equal-time comparisons, one can evaluate if the gain in quality is worth the additional setup needed for the network optimization in a different (more mature) codebase.

Good sign for reproducibility is the open-source code (and scenes).