SIGGRAPH '86 Proceedings of the 13th annual conference on Computer graphics and interactive techniques
Generating antialiased images at low sampling densities
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Antialiased ray tracing by adaptive progressive refinement
SIGGRAPH '89 Proceedings of the 16th annual conference on Computer graphics and interactive techniques
Elements of information theory
Elements of information theory
A perceptually based adaptive sampling algorithm
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
A framework for realistic image synthesis
Communications of the ACM
Statistically optimized sampling for distributed ray tracing
SIGGRAPH '85 Proceedings of the 12th annual conference on Computer graphics and interactive techniques
Antialiasing through stochastic sampling
SIGGRAPH '85 Proceedings of the 12th annual conference on Computer graphics and interactive techniques
An improved illumination model for shaded display
Communications of the ACM
Adaptive Smpling and Bias Estimation in Path Tracing
Proceedings of the Eurographics Workshop on Rendering Techniques '97
Tapestry: A Dynamic Mesh-based Display Representation for Interactive Rendering
Proceedings of the Eurographics Workshop on Rendering Techniques 2000
Refinement criteria based on f-divergences
EGRW '03 Proceedings of the 14th Eurographics workshop on Rendering
Adaptive sampling based on fuzzy inference
Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia
Fuzziness driven adaptive sampling for monte carlo global illuminated rendering
CGI'06 Proceedings of the 24th international conference on Advances in Computer Graphics
A generalized divergence measure for robust image registration
IEEE Transactions on Signal Processing
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Monte Carlo is the only choice for a physically correct method to do global illumination in the field of realistic image synthesis. Generally Monte Carlo based algorithms require a lot of time to eliminate the noise to get an acceptable image. Adaptive sampling is an interesting tool to reduce noise, in which the evaluation of homogeneity of pixel's samples is the key point. In this paper, we propose a new homogeneity measure, namely the arithmetic mean - geometric mean difference (abbreviated to AM - GM difference), which is developed to execute adaptive sampling efficiently. Implementation results demonstrate that our novel adaptive sampling method can perform significantly better than classic ones.