Consistent Post‐Reconstruction for Progressive Photon Mapping. (27th November 2021)

• Record Type: Journal Article
• Authors: Choi, Hajin; Moon, Bochang
• Is Part Of: Computer graphics forum. Volume 40:Number 7(2021)
• Page Start: 121
• Page End: 130
• Publication Date: 2021-11-27
• Subjects: CCS Concepts -- Computing methodologies → Ray tracing; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.14406
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: Photon mapping is a light transport algorithm that simulates various rendering effects (e.g., caustics) robustly, and its progressive variants, progressive photon mapping (PPM) methods, can produce a biased but consistent rendering output. PPM estimates radiance using a kernel density estimation whose parameters (bandwidths) are adjusted progressively, and this refinement enables to reduce its estimation bias. Nonetheless, many iterations (and thus a large number of photons) are often required until PPM produces nearly converged estimates. This paper proposes a post‐reconstruction that improves the performance of PPM by reducing residual errors in PPM estimates. Our key idea is to take multiple PPM estimates with multi‐level correlation structures, and fuse the input images using a weight function trained by supervised learning with maintaining the consistency of PPM. We demonstrate that our technique boosts an existing PPM technique for various rendering scenes.