“Learning to cluster for rendering with many lights” by Wang, Wu, Li and Chuang
Conference:
Type(s):
Title:
- Learning to cluster for rendering with many lights
Session/Category Title: Samping and Denoising
Presenter(s)/Author(s):
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.
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