Robust Adaptive Sampling for Monte-Carlo-based rendering - IRIT
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Robust Adaptive Sampling for Monte-Carlo-based rendering - IRIT
No patterns in the penumbra. Complete scene. Penumbra prone to poor sampling. Complete adaptive sampling algorithm. Uniform sampling. Uniform sampling.
IRIT - University of Toulouse - France
Monte-Carlo rendering - Value of each pixel defined as the expected value of a random variable
Robust Adaptive Sampling for Monte-Carlo-based rendering Anthony Pajot, Loïc Barthe, Mathias Paulin
Relative error and robustness to outliers For each pixel, relative error:
ratio of estimated error and reference error for the corresponding outlier value
1.2
Our proposal: approximate median
1
0.8
- estimated using samples of
Standard estimation: 0.6
average of median of chunks of samples
0.4
Not robust to outliers underestimation
0.2
Goal: focus on bright spots 0
0
0.5
Our goals - Focus processing power where convergence is harder to reach during Monte-Carlo based rendering
3
3.5
Scene prone to bright spots
using
: too uniform
using
: focused on bright spots
Adaptive sampling based on error measure. poor error estimate poor sampling
Previous work and their limitations
Our proposal: alternate adaptive and uniform sampling
Not a relative error: does not take into account dynamic reduction during tonemapping
- Make the error over the pixels uniform at any moment for progressive or time-constrained rendering
- Adaptive sampling based on the statistical nature of the estimation [Purgathofer 1987]
1
Complete scene. Penumbra prone to poor sampling
Uniform sampling
Adaptive sampling, no alternation. Patterns in the penumbra
Adaptive sampling, alternation. No patterns in the penumbra
Complete adaptive sampling algorithm
- Adaptive sampling based on information-theoretic approaches and entropy measures [Xu et al 2007] Does not make the error uniform during rendering, thus less adapted for progressive or time-constrained rendering Both approaches can lead to poor sampling due to low-samples error estimations which underestimate the actual error Uniform sampling
Our approach
Update error and pixel probabilities
Adaptive sampling
Uniform sampling
Comparison with Tsallis entropy [Xu, Sbert, Xinh and Zhan 2007]
- Use a relative error to avoid focusing all processing power on areas receiving more energy. These zones are not necessarily the most noticeable after tonemapping, e.g. undersampling in shadows leads to highly visible noise - Alternate between uniform and adaptive sampling, to ensure that error estimations of all pixels improve during rendering
