Adaptive Polynomial Rendering
by
Bochang Moon,
Steven McDonagh,
Kenny Mitchell,
Markus Gross
To appear in ACM Transactions on Graphics (Proc. of SIGGRAPH 2016)
Abstract
In this paper, we propose a new adaptive rendering method to
improve the performance of Monte Carlo ray tracing, by reducing
noise contained in rendered images while preserving highfrequency
edges. Our method locally approximates an image with
polynomial functions and the optimal order of each polynomial
function is estimated so that our reconstruction error can be minimized.
To robustly estimate the optimal order, we propose a multistage
error estimation process that iteratively estimates our reconstruction
error. In addition, we present an energy-preserving outlier
removal technique to remove spike noise without causing noticeable
energy loss in our reconstruction result. Also, we adaptively
allocate additional ray samples to high error regions guided by our
error estimation. We demonstrate that our approach outperforms
state-of-the-art methods by controlling the tradeoff between reconstruction
bias and variance through locally defining our polynomial
order, even without need for filtering bandwidth optimization, the
common approach of other recent methods.
Contents
Main Report (pdf, 26.6 MB)
Supplementary Report (pdf, 48.1 MB)
Video (mp4, 115 MB)