Zhang Hao, Xu Feng
IEEE Trans Vis Comput Graph. 2018 Dec;24(12):3137-3146. doi: 10.1109/TVCG.2017.2786233. Epub 2017 Dec 28.
Real-time indoor scene reconstruction aims to recover the 3D geometry of an indoor scene in real time with a sensor scanning the scene. Previous works of this topic consider pure static scenes, but in this paper, we focus on more challenging cases that the scene contains dynamic objects, for example, moving people and floating curtains, which are quite common in reality and thus are eagerly required to be handled. We develop an end-to-end system using a depth sensor to scan a scene on the fly. By proposing a Sigmoid-based Iterative Closest Point (S-ICP) method, we decouple the camera motion and the scene motion from the input sequence and segment the scene into static and dynamic parts accordingly. The static part is used to estimate the camera rigid motion, while for the dynamic part, graph node-based motion representation and model-to-depth fitting are applied to reconstruct the scene motions. With the camera and scene motions reconstructed, we further propose a novel mixed voxel allocation scheme to handle static and dynamic scene parts with different mechanisms, which helps to gradually fuse a large scene with both static and dynamic objects. Experiments show that our technique successfully fuses the geometry of both the static and dynamic objects in a scene in real time, which extends the usage of the current techniques for indoor scene reconstruction.
实时室内场景重建旨在通过传感器扫描场景实时恢复室内场景的三维几何结构。此前关于该主题的工作考虑的是纯静态场景，但在本文中，我们关注更具挑战性的情况，即场景中包含动态物体，例如移动的人和飘动的窗帘，这些在现实中很常见，因此迫切需要处理。我们开发了一个端到端系统，使用深度传感器实时扫描场景。通过提出一种基于Sigmoid的迭代最近点（S-ICP）方法，我们从输入序列中解耦相机运动和场景运动，并相应地将场景分割为静态和动态部分。静态部分用于估计相机的刚体运动，而对于动态部分，应用基于图节点的运动表示和模型到深度拟合来重建场景运动。在重建相机和场景运动后，我们进一步提出了一种新颖的混合体素分配方案，以用不同机制处理静态和动态场景部分，这有助于逐步融合包含静态和动态物体的大型场景。实验表明，我们的技术成功地实时融合了场景中静态和动态物体的几何结构，扩展了当前室内场景重建技术的应用范围。
