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A progressive error estimation framework for photon density estimation

Published: 15 December 2010 Publication History

Abstract

We present an error estimation framework for progressive photon mapping. Although estimating rendering error has been established for unbiased rendering algorithms, error estimation for biased rendering algorithms has not been investigated well in comparison. We characterize the error by the sum of a bias estimate and a stochastic noise bound, which is motivated by stochastic error bounds formulation in biased methods. As a part of our error computation, we extend progressive photon mapping to operate with smooth kernels. This enables the calculation of illumination gradients with arbitrary accuracy, which we use to progressively compute the local bias in the radiance estimate. We also show how variance can be computed in progressive photon mapping, which is used to estimate the error due to noise. As an example application, we show how our error estimation can be used to compute images with a given error threshold. For this example application, our framework only requires the error threshold and a confidence level to automatically terminate rendering. Our results demonstrate how our error estimation framework works well in realistic synthetic scenes.

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      cover image ACM Transactions on Graphics
      ACM Transactions on Graphics  Volume 29, Issue 6
      December 2010
      480 pages
      ISSN:0730-0301
      EISSN:1557-7368
      DOI:10.1145/1882261
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 15 December 2010
      Published in TOG Volume 29, Issue 6

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      Author Tags

      1. density estimation
      2. photon mapping

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      • (2024)Differentiable Photon Mapping using Generalized Path GradientsACM Transactions on Graphics10.1145/368795843:6(1-15)Online publication date: 19-Dec-2024
      • (2024)Practical Error Estimation for Denoised Monte Carlo Image SynthesisACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657511(1-10)Online publication date: 13-Jul-2024
      • (2024)Hypothesis Testing for Progressive Kernel Estimation and VCM FrameworkIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.327459530:8(4709-4723)Online publication date: 1-Aug-2024
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      • (2021)Correlation‐Aware Multiple Importance Sampling for Bidirectional Rendering AlgorithmsComputer Graphics Forum10.1111/cgf.14262840:2(231-238)Online publication date: 4-Jun-2021
      • (2020)CPPMACM Transactions on Graphics10.1145/3414685.341782239:6(1-12)Online publication date: 27-Nov-2020
      • (2020)Deep Kernel Density Estimation for Photon MappingComputer Graphics Forum10.1111/cgf.1405239:4(35-45)Online publication date: 20-Jul-2020
      • (2020)Progressive Photon Elimination With a Status TreeIEEE Access10.1109/ACCESS.2020.29665498(12735-12744)Online publication date: 2020
      • (2018)An improved multiple importance sampling heuristic for density estimates in light transport simulationsProceedings of the Eurographics Symposium on Rendering: Experimental Ideas & Implementations10.2312/sre.20181173(65-72)Online publication date: 1-Jul-2018
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