This paper proposes a real-time dynamic scene reconstruction method capable
of reproducing the motion, geometry, and segmentation simultaneously given live
depth stream from a single RGB-D camera. Our approach fuses geometry frame by
frame and uses a segmentation-enhanced node graph structure to drive the
deformation of geometry in registration step. A two-level node motion
optimization is proposed. The optimization space of node motions and the range
of physically-plausible deformations are largely reduced by taking advantage of
the articulated motion prior, which is solved by an efficient node graph
segmentation method. Compared to previous fusion-based dynamic scene
reconstruction methods, our experiments show robust and improved reconstruction
results for tangential and occluded motions.

本文提出了一种实时动态场景重构方法，能够在给定单个 RGB-D 相机的实时深度流情况下同时再现运动、几何和分割。我们的方法通过融合几何逐帧进行处理，并使用分割增强的节点图结构来驱动几何变形。我们提出了一种二级节点运动优化方法，并通过利用关节运动先验知识来大大减少节点运动的优化空间和物理合理的变形范围。与之前基于融合的动态场景重构方法相比，我们的实验表明，我们的方法对于切向运动和遮挡的运动都具有鲁棒性和改进的重建结果。