{"title":"二维轮廓的三维增强实现平面彩色绘图的自动照明","authors":"D. Tschumperlé, C. Porquet, A. Mahboubi","doi":"10.1109/ICIP46576.2022.9897386","DOIUrl":null,"url":null,"abstract":"In this paper, a new automatic method for the illumination of flat-colored drawings is proposed. First, we reconstruct a 3D augmentation of a 2D silhouette from the analysis of its skeleton. Then, we apply the Phong lighting model that relies on the estimated normal map to generate an illuminated drawing. This method compares favorably to recent state-of-the-art methods, e.g. those using convolutional neural networks.","PeriodicalId":387035,"journal":{"name":"2022 IEEE International Conference on Image Processing (ICIP)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automatic Illumination of Flat-Colored Drawings by 3D Augmentation of 2D Silhouettes\",\"authors\":\"D. Tschumperlé, C. Porquet, A. Mahboubi\",\"doi\":\"10.1109/ICIP46576.2022.9897386\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a new automatic method for the illumination of flat-colored drawings is proposed. First, we reconstruct a 3D augmentation of a 2D silhouette from the analysis of its skeleton. Then, we apply the Phong lighting model that relies on the estimated normal map to generate an illuminated drawing. This method compares favorably to recent state-of-the-art methods, e.g. those using convolutional neural networks.\",\"PeriodicalId\":387035,\"journal\":{\"name\":\"2022 IEEE International Conference on Image Processing (ICIP)\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Image Processing (ICIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIP46576.2022.9897386\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Image Processing (ICIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP46576.2022.9897386","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic Illumination of Flat-Colored Drawings by 3D Augmentation of 2D Silhouettes
In this paper, a new automatic method for the illumination of flat-colored drawings is proposed. First, we reconstruct a 3D augmentation of a 2D silhouette from the analysis of its skeleton. Then, we apply the Phong lighting model that relies on the estimated normal map to generate an illuminated drawing. This method compares favorably to recent state-of-the-art methods, e.g. those using convolutional neural networks.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
