Xu Lan, Cheng Wei, Guo Kaiwen, Han Lei, Liu Yebin, Fang Lu
IEEE Trans Vis Comput Graph. 2021 Jan;27(1):68-82. doi: 10.1109/TVCG.2019.2930691. Epub 2020 Nov 24.
While dynamic scene reconstruction has made revolutionary progress from the earliest setup using a mass of static cameras in studio environment to the latest egocentric or hand-held moving camera based schemes, it is still restricted by the recording volume, user comfortability, human labor and expertise. In this paper, a novel solution is proposed through a real-time and robust dynamic fusion scheme using a single flying depth camera, denoted as FlyFusion. By proposing a novel topology compactness strategy for effectively regularizing the complex topology changes, and the Geometry And Motion Energy (GAME) metric for guiding the viewpoint optimization in the volumetric space, FlyFusion succeeds to enable intelligent viewpoint selection based on the immediate dynamic reconstruction result. The merit of FlyFusion lies in its concurrent robustness, efficiency, and adaptation in producing fused and denoised 3D geometry and motions of a moving target interacting with different non-rigid objects in a large space.
虽然动态场景重建已经取得了革命性的进展,从最早在工作室环境中使用大量静态相机的设置,到最新的以自我为中心或基于手持移动相机的方案,但它仍然受到记录体积、用户舒适度、人工劳动和专业知识的限制。在本文中,通过使用单个飞行深度相机的实时且强大的动态融合方案提出了一种新颖的解决方案,称为FlyFusion。通过提出一种新颖的拓扑紧凑性策略来有效地规范复杂的拓扑变化,以及用于在体积空间中指导视点优化的几何与运动能量(GAME)度量,FlyFusion成功地基于即时动态重建结果实现了智能视点选择。FlyFusion的优点在于其在生成与大空间中不同非刚性物体相互作用的移动目标的融合和去噪3D几何形状及运动方面具有同时的鲁棒性、效率和适应性。