IEEE Trans Cybern. 2013 Oct;43(5):1347-56. doi: 10.1109/TCYB.2013.2276430. Epub 2013 Aug 21.
Real-time 3-D reconstruction of the human body has many applications in anthropometry, telecommunications, gaming, fashion, and other areas of human-computer interaction. In this paper, a novel framework is presented for reconstructing the 3-D model of the human body from a sequence of RGB-D frames. The reconstruction is performed in real time while the human subject moves arbitrarily in front of the camera. The method employs a novel parameterization of cylindrical-type objects using Cartesian tensor and b-spline bases along the radial and longitudinal dimension respectively. The proposed model, dubbed tensor body, is fitted to the input data using a multistep framework that involves segmentation of the different body regions, robust filtering of the data via a dynamic histogram, and energy-based optimization with positive-definite constraints. A Riemannian metric on the space of positive-definite tensor splines is analytically defined and employed in this framework. The efficacy of the presented methods is demonstrated in several real-data experiments using the Microsoft Kinect sensor.
实时人体 3D 重建在人体测量学、电信、游戏、时尚和其他人机交互领域有很多应用。在本文中，提出了一种从一系列 RGB-D 帧中重建人体 3D 模型的新框架。在人体主体在摄像机前任意移动的情况下，实时进行重建。该方法使用一种新颖的圆柱型物体参数化方法，使用笛卡尔张量和 B 样条基分别沿径向和纵向维度。所提出的模型，称为张量体，使用涉及不同身体区域分割、通过动态直方图进行稳健滤波以及基于能量的具有正定约束的优化的多步骤框架拟合输入数据。在这个框架中，分析定义了正定张量样条空间上的黎曼度量。使用 Microsoft Kinect 传感器的几个真实数据实验证明了所提出方法的有效性。
