https://scholars.lib.ntu.edu.tw/handle/123456789/632242
Title: | Learning to Cluster for Rendering with Many Lights | Authors: | Wang Y.-C Wu Y.-T Li T.-M YUNG-YU CHUANG |
Keywords: | Direct illumination; Many-light rendering; Optimization theory; Ray tracing; Reinforcement learning | Issue Date: | 2021 | Journal Volume: | 40 | Journal Issue: | 6 | Source: | ACM Transactions on Graphics | Abstract: | We present an unbiased online Monte Carlo method for rendering with many lights. Our method adapts both the hierarchical light clustering and the sampling distribution to our collected samples. Designing such a method requires us to make clustering decisions under noisy observation, and making sure that the sampling distribution adapts to our target. Our method is based on two key ideas: a coarse-to-fine clustering scheme that can find good clustering configurations even with noisy samples, and a discrete stochastic successive approximation method that starts from a prior distribution and provably converges to a target distribution. We compare to other state-of-the-art light sampling methods, and show better results both numerically and visually. © 2021 Copyright held by the owner/author(s). |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125706312&doi=10.1145%2f3478513.3480561&partnerID=40&md5=40c8fece7bb04e4c9f18a3576000436d https://scholars.lib.ntu.edu.tw/handle/123456789/632242 |
ISSN: | 7300301 | DOI: | 10.1145/3478513.3480561 | SDG/Keyword: | Approximation theory; Computation theory; Monte Carlo methods; Site selection; Stochastic systems; Clustering scheme; Clusterings; Coarse to fine; Direct illumination; Many-light rendering; MonteCarlo methods; Noisy observations; Online monte carlo; Optimization theory; Sampling distribution; Reinforcement learning |
Appears in Collections: | 資訊工程學系 |
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