3 ms·
Naive raytracers fall down on many edge-cases and need a ton of optimizations that might not quite qualify as hacks but still drive up complexity a lot. And the
by the8472 6y ago
Naive raytracers fall down on many edge-cases and need a ton of optimizations that might not quite qualify as hacks but still drive up complexity a lot. And then you can add proper hacks like ML-based denoising on top.
- slx26 6y agoHi, could you or someone else with the right knowledge give some examples of those optimizations/hacks for ray tracers or point me to some place that explains those in more detail? Thanks in advance.
- the8472 6y agoThat's an entire research/engineering field. I guess you can read/view SIGGRAPH papers and presentations to get an idea what they're doing to achieve realtime performance. Common things that make a scene too difficult to handle for brute force sampling approaches: caustics, difficult to reach light sources (small or behind openings), many light sources, camera or motion blur, volumetric materials, etc. Algorithms approximating the behavior of real materials also is a complex topic, e.g. skin or hair are handled separately from dielectric or metallic surface models. Some optimizations can solve one problem well in isolation but fail when encountering a combination of them. Here's one that tries to solve caustics and small light sources at the same time: https://cgg.mff.cuni.cz/~jaroslav/papers/2012-vcm/2012-vcm-paper.pdf https://cgg.mff.cuni.cz/~jaroslav/papers/2012-vcm/2012-vcm-p...
- slx26 6y agoThat was very informative and accessible, thanks again!