Adaptive Polynomial Rendering

Bochang Moon1, Steven McDonagh1, Kenny Mitchell1,2, Markus Gross1,3,

Disney Research1, Edinburgh Napier University2, ETH Zurich3

ACM Transactions on Graphics (Proc. of SIGGRAPH 2016)

(a) Ours, 16 spp
rMSE 0.00541
(b) MC input, 16 spp
rMSE 0.68832
(c) Ours, 16 spp
rMSE 0.00541
(d) Reference, 32K spp
Our adaptive rendering result for the Hotel Lobby scene. Given a noisy input image using a small number of samples per pixel (spp) (b), our method (a) and (c) effectively removes MC noise while avoiding excessive blurring by performing a novel reconstruction using adaptively chosen polynomials. In addition, our method drastically reduces the relative mean squared error (rMSE) of the input (b).


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.

Video (available to download in the last section)