Feature Generation for Adaptive Gradient‐Domain Path Tracing. (24th October 2018)

• Record Type: Journal Article
• Authors: Back, Jonghee; Yoon, Sung‐Eui; Moon, Bochang
• Is Part Of: Computer graphics forum. Volume 37:Number 7(2018)
• Page Start: 65
• Page End: 74
• Publication Date: 2018-10-24
• Subjects: CCS Concepts -- Computing methodologies → Ray tracing; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.13548
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: In this paper, we propose a new technique to incorporate recent adaptive rendering approaches built upon local regression theory into a gradient‐domain path tracing framework, in order to achieve high‐quality rendering results. Our method aims to reduce random artifacts introduced by random sampling on image colors and gradients. Our high‐level approach is to identify a feature image from noisy gradients, and pass the image to an existing local regression based adaptive method so that adaptive sampling and reconstruction using our feature can boost the performance of gradient‐domain rendering. To fulfill our idea, we derive an ideal feature in the form of image gradients and propose an estimation process for the ideal feature in the presence of noise in image gradients. We demonstrate that our integrated adaptive solution leads to performance improvement for a gradient‐domain path tracer, by seamlessly incorporating recent adaptive sampling and reconstruction strategies through our estimated feature.