{"title":"一个综合的多光源数据集用于内在图像算法的基准测试","authors":"Shida Beigpour, A. Kolb, Sven Kunz","doi":"10.1109/ICCV.2015.28","DOIUrl":null,"url":null,"abstract":"In this paper, we provide a new, real photo dataset with precise ground-truth for intrinsic image research. Prior ground-truth datasets have been restricted to rather simple illumination conditions and scene geometries, or have been enhanced using image synthesis methods. The dataset provided in this paper is based on complex multi-illuminant scenarios under multi-colored illumination conditions and challenging cast shadows. We provide full per-pixel intrinsic ground-truth data for these scenarios, i.e. reflectance, specularity, shading, and illumination for scenes as well as preliminary depth information. Furthermore, we evaluate 3 state-of-the-art intrinsic image recovery methods, using our dataset.","PeriodicalId":6633,"journal":{"name":"2015 IEEE International Conference on Computer Vision (ICCV)","volume":"48 1","pages":"172-180"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"A Comprehensive Multi-Illuminant Dataset for Benchmarking of the Intrinsic Image Algorithms\",\"authors\":\"Shida Beigpour, A. Kolb, Sven Kunz\",\"doi\":\"10.1109/ICCV.2015.28\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we provide a new, real photo dataset with precise ground-truth for intrinsic image research. Prior ground-truth datasets have been restricted to rather simple illumination conditions and scene geometries, or have been enhanced using image synthesis methods. The dataset provided in this paper is based on complex multi-illuminant scenarios under multi-colored illumination conditions and challenging cast shadows. We provide full per-pixel intrinsic ground-truth data for these scenarios, i.e. reflectance, specularity, shading, and illumination for scenes as well as preliminary depth information. Furthermore, we evaluate 3 state-of-the-art intrinsic image recovery methods, using our dataset.\",\"PeriodicalId\":6633,\"journal\":{\"name\":\"2015 IEEE International Conference on Computer Vision (ICCV)\",\"volume\":\"48 1\",\"pages\":\"172-180\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Computer Vision (ICCV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCV.2015.28\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Computer Vision (ICCV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCV.2015.28","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Comprehensive Multi-Illuminant Dataset for Benchmarking of the Intrinsic Image Algorithms
In this paper, we provide a new, real photo dataset with precise ground-truth for intrinsic image research. Prior ground-truth datasets have been restricted to rather simple illumination conditions and scene geometries, or have been enhanced using image synthesis methods. The dataset provided in this paper is based on complex multi-illuminant scenarios under multi-colored illumination conditions and challenging cast shadows. We provide full per-pixel intrinsic ground-truth data for these scenarios, i.e. reflectance, specularity, shading, and illumination for scenes as well as preliminary depth information. Furthermore, we evaluate 3 state-of-the-art intrinsic image recovery methods, using our dataset.