Adaptive polynomial rendering

Moon, Bochang; McDonagh, Steven; Mitchell, Kenny; Gross, Markus

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 high-frequency 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 multi-stage 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.

Citation

Moon, B., McDonagh, S., Mitchell, K., & Gross, M. (2016, July). Adaptive polynomial rendering. Presented at ACM SIGGRAPH 2016, Anaheim, California, US

• Presentation Conference Type: Conference Paper (published)
• Conference Name: ACM SIGGRAPH 2016
• Start Date: Jul 24, 2016
• End Date: Jul 28, 2016
• Acceptance Date: Mar 30, 2016
• Online Publication Date: Jul 24, 2016
• Publication Date: Jul 11, 2016
• Deposit Date: Jun 23, 2017
• Journal: ACM Transactions on Graphics
• Print ISSN: 0730-0301
• Electronic ISSN: 1557-7368
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 35
• Issue: 4
• Article Number: 40
• DOI: https://doi.org/10.1145/2897824.2925936
• Keywords: Computer Graphics, Computer-Aided Design,
• Public URL: http://researchrepository.napier.ac.uk/Output/951547