{"title":"来自环境地图的光路平面采样","authors":"H. Dammertz, Johannes Hanika","doi":"10.1080/2151237X.2009.10129280","DOIUrl":null,"url":null,"abstract":"We present a method to start light paths on an emitting environment map in the context of Monte Carlo global illumination with image-based lighting or physical sky models. With this technique, we can efficiently render unbiased caustics from these kinds of lights, for example using bi-directional path tracing. Additionally, it is now possible to use algorithms like multiple importance sampling, photon mapping, and instant radiosity correctly with environment maps.","PeriodicalId":354935,"journal":{"name":"Journal of Graphics, GPU, and Game Tools","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Plane Sampling for Light Paths from the Environment Map\",\"authors\":\"H. Dammertz, Johannes Hanika\",\"doi\":\"10.1080/2151237X.2009.10129280\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a method to start light paths on an emitting environment map in the context of Monte Carlo global illumination with image-based lighting or physical sky models. With this technique, we can efficiently render unbiased caustics from these kinds of lights, for example using bi-directional path tracing. Additionally, it is now possible to use algorithms like multiple importance sampling, photon mapping, and instant radiosity correctly with environment maps.\",\"PeriodicalId\":354935,\"journal\":{\"name\":\"Journal of Graphics, GPU, and Game Tools\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Graphics, GPU, and Game Tools\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/2151237X.2009.10129280\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Graphics, GPU, and Game Tools","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/2151237X.2009.10129280","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Plane Sampling for Light Paths from the Environment Map
We present a method to start light paths on an emitting environment map in the context of Monte Carlo global illumination with image-based lighting or physical sky models. With this technique, we can efficiently render unbiased caustics from these kinds of lights, for example using bi-directional path tracing. Additionally, it is now possible to use algorithms like multiple importance sampling, photon mapping, and instant radiosity correctly with environment maps.