{"title":"高效大都市路径采样材料编辑和重新渲染","authors":"Tomoya Yamaguchi, Tatsuya Yatagawa, S. Morishima","doi":"10.2312/pg.20181271","DOIUrl":null,"url":null,"abstract":"This paper proposes efficient path sampling for re-rendering scenes after material editing. The proposed sampling method is based on Metropolis light transport (MLT) and distributes more path samples to pixels whose values have been changed significantly by editing. First, we calculate the difference between images before and after editing to estimate the changes in pixel values. In this step, we render the difference image directly rather than calculating the difference in the images by separately rendering the images before and after editing. Then, we sample more paths for pixels with larger difference values and render the scene after editing by reducing variances of Monte Carlo estimators using the control variates. Thus, we can obtain rendering results with a small amount of noise using only a small number of path samples. We examine the proposed sampling method with a range of scenes and demonstrate that it achieves lower estimation errors and variances over the state-of-the-art methods.","PeriodicalId":88304,"journal":{"name":"Proceedings. Pacific Conference on Computer Graphics and Applications","volume":"865 1","pages":"21-24"},"PeriodicalIF":0.0000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Efficient Metropolis Path Sampling for Material Editing and Re-rendering\",\"authors\":\"Tomoya Yamaguchi, Tatsuya Yatagawa, S. Morishima\",\"doi\":\"10.2312/pg.20181271\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes efficient path sampling for re-rendering scenes after material editing. The proposed sampling method is based on Metropolis light transport (MLT) and distributes more path samples to pixels whose values have been changed significantly by editing. First, we calculate the difference between images before and after editing to estimate the changes in pixel values. In this step, we render the difference image directly rather than calculating the difference in the images by separately rendering the images before and after editing. Then, we sample more paths for pixels with larger difference values and render the scene after editing by reducing variances of Monte Carlo estimators using the control variates. Thus, we can obtain rendering results with a small amount of noise using only a small number of path samples. We examine the proposed sampling method with a range of scenes and demonstrate that it achieves lower estimation errors and variances over the state-of-the-art methods.\",\"PeriodicalId\":88304,\"journal\":{\"name\":\"Proceedings. Pacific Conference on Computer Graphics and Applications\",\"volume\":\"865 1\",\"pages\":\"21-24\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. Pacific Conference on Computer Graphics and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2312/pg.20181271\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. Pacific Conference on Computer Graphics and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2312/pg.20181271","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
本文提出了一种有效的路径采样方法,用于材质编辑后场景的重新渲染。该采样方法基于Metropolis light transport (MLT),将更多的路径样本分布到经过编辑后值发生显著变化的像素上。首先,我们计算编辑前后图像的差值来估计像素值的变化。在这一步中,我们直接渲染差异图像,而不是通过分别渲染编辑前后的图像来计算图像中的差异。然后,我们对具有较大差值的像素进行更多路径采样,并通过使用控制变量减少蒙特卡罗估计器的方差来渲染编辑后的场景。因此,我们可以使用少量的路径样本获得具有少量噪声的渲染结果。我们用一系列场景检验了所提出的采样方法,并证明它比最先进的方法实现了更低的估计误差和方差。
Efficient Metropolis Path Sampling for Material Editing and Re-rendering
This paper proposes efficient path sampling for re-rendering scenes after material editing. The proposed sampling method is based on Metropolis light transport (MLT) and distributes more path samples to pixels whose values have been changed significantly by editing. First, we calculate the difference between images before and after editing to estimate the changes in pixel values. In this step, we render the difference image directly rather than calculating the difference in the images by separately rendering the images before and after editing. Then, we sample more paths for pixels with larger difference values and render the scene after editing by reducing variances of Monte Carlo estimators using the control variates. Thus, we can obtain rendering results with a small amount of noise using only a small number of path samples. We examine the proposed sampling method with a range of scenes and demonstrate that it achieves lower estimation errors and variances over the state-of-the-art methods.