{"title":"PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes","authors":"Yu-Ying Yeh, Zhengqin Li, Yannick Hold-Geoffroy, Rui Zhu, Zexiang Xu, Miloš Hašan, Kalyan Sunkavalli, Manmohan Chandraker","doi":"10.1109/CVPR52688.2022.01801","DOIUrl":null,"url":null,"abstract":"Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout. In this work, we go beyond this to propose PhotoScene11Code: https://github.com/ViLab-UCSD/PhotoScene, a framework that takes input image(s) of a scene along with approximately aligned CAD geometry (either reconstructed automatically or manually specified) and builds a photorealistic digital twin with high-quality materials and similar lighting. We model scene materials using procedural material graphs; such graphs represent photorealistic and resolution-independent materials. We optimize the parameters of these graphs and their texture scale and rotation, as well as the scene lighting to best match the input image via a differentiable rendering layer. We evaluate our technique on objects and layout reconstructions from ScanNet, SUN RGB-D and stock photographs, and demonstrate that our method reconstructs high-quality, fully relightable 3D scenes that can be re-rendered under arbitrary viewpoints, zooms and lighting.","PeriodicalId":355552,"journal":{"name":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"58 30","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPR52688.2022.01801","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout. In this work, we go beyond this to propose PhotoScene11Code: https://github.com/ViLab-UCSD/PhotoScene, a framework that takes input image(s) of a scene along with approximately aligned CAD geometry (either reconstructed automatically or manually specified) and builds a photorealistic digital twin with high-quality materials and similar lighting. We model scene materials using procedural material graphs; such graphs represent photorealistic and resolution-independent materials. We optimize the parameters of these graphs and their texture scale and rotation, as well as the scene lighting to best match the input image via a differentiable rendering layer. We evaluate our technique on objects and layout reconstructions from ScanNet, SUN RGB-D and stock photographs, and demonstrate that our method reconstructs high-quality, fully relightable 3D scenes that can be re-rendered under arbitrary viewpoints, zooms and lighting.
大多数室内三维场景重建方法侧重于恢复三维几何和场景布局。在这项工作中,我们超越了这一点,提出PhotoScene11Code: https://github.com/ViLab-UCSD/PhotoScene,这是一个框架,它将场景的输入图像与近似对齐的CAD几何形状(自动重建或手动指定)一起使用,并构建具有高质量材料和类似照明的逼真数字双胞胎。我们使用程序材质图对场景材质建模;这样的图形表示逼真的和分辨率无关的材料。我们通过可微分渲染层优化这些图形的参数及其纹理比例和旋转,以及场景照明,以最佳地匹配输入图像。我们评估了我们的技术对象和布局重建从ScanNet, SUN RGB-D和库存照片,并证明我们的方法重建高质量,完全可照明的3D场景,可以在任意视点,变焦和照明下重新渲染。