Hybrid 3D Reconstruction for Geometry-Based Free Viewpoint Video

基于几何的自由视点视频的混合 3D 重建

基本信息

批准号：
232339705
负责人：
Professor Dr.-Ing. Thomas Sikora
金额：
--
依托单位：
Fachgebiet Nachrichtenübertragung
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2013
资助国家：
德国
起止时间：
2012-12-31 至 2015-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/232339705?language=en
关键词：
Hybrid 3D Reconstruction Geometry Based

项目摘要

Our purpose is to develop algorithms for capturing time-varying 3D scene geometry in the context of Free Viewpoint Video applications. This requires a large working volume of at least 2.5m x 2.5m x 3m for capturing a scene. Our strategy involves the generation of a 3D computer graphics model from the captured scene geometry. This model is used at the receiver to reconstruct full parallax Free Viewpoint stereoscopic video. In order to display the time- and view-varying 3D content of the scene on stereoscopic or multi-view displays, it is important that solutions are real-time capable and allow capture of 3D computer graphics models that are as complete and as accurate as possible. Our research strategy attempts to overcome limitations of active and passive techniques for image reconstruction by combining information from both techniques in a hybrid sensor fusion approach. To this end, we employ a multi-projector multi-camera system to project structured light onto the scene and a phase-shifting technique to extract depth and texture information in an active vision approach. The passive multi-camera system is then used to arrive at additional disparity information, which will improve depth and texture reconstruction of the 3D scene model. In order to reduce texture dependent artifacts that stem from large aperture cameras we will derive deconvolution algorithms that use the inverse of an estimated spatially varying point-spread function.

我们的目的是开发用于在免费观点视频应用程序的背景下捕获时变的3D场景几何的算法。这需要大量的工作量至少为2.5m x 2.5m x 3m，以捕获场景。我们的策略涉及从捕获的场景几何形状中生成3D计算机图形模型。该模型在接收器中用于重建完整的视差免费视点立体视频。为了在立体或多视图显示器上显示场景中场景的时间和视图变量的3D内容，重要的是，解决方案具有实时能力并允许捕获尽可能完整且尽可能准确的3D计算机图形模型。我们的研究策略试图通过在混合传感器融合方法中结合两种技术的信息来克服图像重建的主动和被动技术的局限性。为此，我们采用多项目的多摄像机系统将结构化的光投射到场景上，并采用相移技术，以主动视觉方法提取深度和纹理信息。然后，使用被动多相机系统来获得其他差异信息，这将改善3D场景模型的深度和纹理重建。为了减少依赖大量光圈摄像机的纹理依赖性伪像，我们将得出使用估计的空间变化点传播函数的反向卷积算法。