Hybrid 3D Reconstruction for Geometry-Based Free Viewpoint Video

我们的目的是开发用于在自由视点视频应用的背景下捕获时变3D场景几何形状的算法。这需要至少2.5m x 2.5m x 3m的大工作体积来捕获场景。我们的策略涉及到从捕获的场景几何生成一个三维计算机图形模型。该模型在接收端用于重构全视差自由视点立体视频。为了在立体或多视图显示器上显示场景的随时间和视图变化的3D内容，重要的是解决方案能够实时并且允许捕获尽可能完整和准确的3D计算机图形模型。我们的研究策略试图克服的局限性，主动和被动技术的图像重建相结合的混合传感器融合方法从这两种技术的信息。为此，我们采用了多投影仪多摄像机系统，以项目结构光到场景和相移技术，以提取深度和纹理信息的主动视觉方法。然后使用被动多相机系统来获得额外的视差信息，这将改善3D场景模型的深度和纹理重建。为了减少纹理相关的文物，干从大孔径相机，我们将推导出反卷积算法，使用估计的空间变化的点扩散函数的逆。

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.