The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Optimally combining sampling techniques for Monte Carlo rendering
Open accessAbstract
Monte Carlo integration is a powerful technique for the evaluation of difficult integrals.Applications in rendering include distribution ray tracing, Monte Carlo path tracing, and form-factor computation for radiosity methods.In these cases variance can often be significantly reduced by drawing samples from several distributions, each designed to sample well some difficult aspect of the integrand.Normally this is done by explicitly partitioning the integration domain into regions that are sampled differently.We present a powerful alternative for constructing robust Monte Carlo estimators, by combining samples from several distributions in a way that is provably good.These estimators are unbiased, and can reduce variance significantly at little additional cost.We present experiments and measurements from several areas in rendering: calculation of glossy highlights from area light sources, the "final gather" pass of some radiosity algorithms, and direct solution of the rendering equation using bidirectional path tracing.
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Cites
Kajiya, J. T. (1986) · ACM SIGGRAPH Computer Graphics · doi:10.1145/15886.15902 · Cited by 2,238
Kalos, M. H., & Whitlock, P. A. (1986) · Wiley eBooks · doi:10.1002/9783527617395 · Cited by 1,546
Cook, R. L., Porter, T., & Carpenter, L. C. (1984) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/800031.808590 · Cited by 858
Lafortune, E. P., & Willems, Y. (1993) · Cited by 323
Bidirectional Estimators for Light Transport
Veach, E., & Guibas, L. (1995) · Springer eBooks · doi:10.1007/978-3-642-87825-1_11 · Cited by 275
Self-citationMonte Carlo techniques for direct lighting calculations
Shirley, P., Wang, C., & Zimmerman, K. (1996) · ACM Transactions on Graphics · doi:10.1145/226150.226151 · Cited by 211
Unbiased sampling techniques for image synthesis
Kirk, D. B., & Arvo, J. R. (1991) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/122718.122735 · Cited by 67
A progressive multi-pass method for global illumination
Chen, S. E., Rushmeier, H., Miller, G., & Turner, D. (1991) · ACM SIGGRAPH Computer Graphics · doi:10.1145/127719.122737 · Cited by 47
Cited by
The bridge test for sampling narrow passages with probabilistic roadmap planners
Hsu, D., Jiang, T., Reif, J., & Sun, Z. (2004) · 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422) · doi:10.1109/robot.2003.1242285 · Cited by 355
Improving SIMD efficiency for parallel Monte Carlo light transport on the GPU
van Antwerpen, D. (2011) · Proceedings of the ACM SIGGRAPH Symposium on High Performance Graphics · doi:10.1145/2018323.2018330 · Cited by 40
Metadata
- Authors
- Veach, E., & Guibas, L. J.
- Venue
- Proceedings of the 22nd annual conference on Computer graphics and interactive techniques - SIGGRAPH '95
- Publisher
- ACM Press
- Published
- 1995
- Pages
- 419-428
- OpenAlex
- W2090346540
- Cited by
- 616
- References
- 8
- FWCI
- 7.28
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: Optimally combining sampling techniques for Monte Carlo rendering. https://app.cochat.ai/papers/10.1145/218380.218498
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- Veach, E., & Guibas, L. J. (1995). Optimally combining sampling techniques for Monte Carlo rendering. Proceedings of the 22nd annual conference on Computer graphics and interactive techniques - SIGGRAPH '95. https://doi.org/10.1145/218380.218498
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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