Multilevel Monte Carlo for asymptotically efficient path tracing.
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| Název: | Multilevel Monte Carlo for asymptotically efficient path tracing. |
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| Autoři: | Lee, Wonjun1 (AUTHOR) wonjunlee.0729@gmail.com |
| Zdroj: | Visual Computer. Jul2025, Vol. 41 Issue 9, p6335-6348. 14p. |
| Témata: | *SPATIAL data structures, *SAMPLE size (Statistics) |
| Abstrakt: | Efficiency improvement techniques are widely used to improve the computational efficiency of Monte Carlo path tracing. While numerous methods have been proposed with different strategies, they fundamentally aim to control the number of samples at each depth to improve efficiency. To achieve this, previous approaches sample contributions at each vertex of the incrementally constructed path and employ Russian roulette and splitting to adjust the sample count at each depth. However, additional errors introduced by correlations from path sharing can limit the potential efficiency of these approaches by increasing the number of samples needed to optimize efficiency. To address this limitation, we propose an alternative efficiency improvement technique for Monte Carlo path tracing using multilevel Monte Carlo. We start by defining a multilevel estimator that sums independent Monte Carlo estimators, each of which samples contributions at a specified depth. Then, the efficiency is optimized by adjusting each estimator's sample size, eliminating the need for spatial data structures. While this multilevel setup increases sampling costs, it reduces variance by removing correlations between contributions. Essentially, the reduced variance leads to substantial performance gains by reducing the number of samples required for efficiency optimization. Consequently, our approach achieves noticeable speedup over the state-of-the-art methods without relying on complex spatial data structures. [ABSTRACT FROM AUTHOR] |
| Databáze: | Academic Search Index |
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Nájsť tento článok vo Web of Science | Abstrakt: | Efficiency improvement techniques are widely used to improve the computational efficiency of Monte Carlo path tracing. While numerous methods have been proposed with different strategies, they fundamentally aim to control the number of samples at each depth to improve efficiency. To achieve this, previous approaches sample contributions at each vertex of the incrementally constructed path and employ Russian roulette and splitting to adjust the sample count at each depth. However, additional errors introduced by correlations from path sharing can limit the potential efficiency of these approaches by increasing the number of samples needed to optimize efficiency. To address this limitation, we propose an alternative efficiency improvement technique for Monte Carlo path tracing using multilevel Monte Carlo. We start by defining a multilevel estimator that sums independent Monte Carlo estimators, each of which samples contributions at a specified depth. Then, the efficiency is optimized by adjusting each estimator's sample size, eliminating the need for spatial data structures. While this multilevel setup increases sampling costs, it reduces variance by removing correlations between contributions. Essentially, the reduced variance leads to substantial performance gains by reducing the number of samples required for efficiency optimization. Consequently, our approach achieves noticeable speedup over the state-of-the-art methods without relying on complex spatial data structures. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 01782789 |
| DOI: | 10.1007/s00371-025-03935-4 |