3 ms·
This surprises me. With Monte Carlo methods, wouldn't you rather want quasi-random numbers[0], because they allow the simulation to converge asymptotically fas
by Athas 6y ago
This surprises me. With Monte Carlo methods, wouldn't you rather want quasi-random numbers[0], because they allow the simulation to converge asymptotically faster? Those are certainly not anything like crypto-strength.
[0]: https://en.wikipedia.org/wiki/Low-discrepancy_sequence https://en.wikipedia.org/wiki/Low-discrepancy_sequence
- 0-_-0 6y agoThis is my understanding as well. In a classic MC problem, global illumination with path tracing, low discrepancy-based sampling converges about twice as fast as random numbers. The Cycles renderer in Blender doesn't even give you the option to use random numbers, just a choice of low discrepancy sequnces [1]. [1] "Pattern" section here: http://builder.openhmd.net/blender-hmd-viewport-temp/render/cycles/settings/integrator.html#sampling http://builder.openhmd.net/blender-hmd-viewport-temp/render/...
- MauranKilom 6y agoThe suggested constants for additive recurrence in that wiki article have given me grief in the past. It is surprisingly simple to do extremely well with better choice of constants: http://extremelearning.com.au/unreasonable-effectiveness-of-quasirandom-sequences/ http://extremelearning.com.au/unreasonable-effectiveness-of-... (yes, no HTTPS, but incredibly illustrative) Discussed in 2018: https://news.ycombinator.com/item?id=17873284 https://news.ycombinator.com/item?id=17873284