With the development of hardwares such as mobile devices and portable depth cameras, on-line 3D reconstruction on the mobile devices with depth streams as input turns to be possible and promising. Most existing systems use volumetric representation methods to fuse the depth images and use ICP algorithm to estimate the poses of cameras. However, ICP tracker suffers from large drift in scenes containing insufficient geometric information. To deal with this problem, we propose a stability based sampling method which select different number of point-pairs in different windows according to their geometric stability. In addition, we fuse the ICP tracker with the IMU information through an analysis of the condition number. Then we apply the stability based sampling method to the spatially hashed volumetric representation. Qualitative and quantitative evaluations of tracking accuracy and 3D reconstruction results show that our method outperforms the current state-of-the-art systems, especially in scenes lacking sufficient geometric information. In total, our method achieves frame rates 20 Hz on an Apple iPad Air 2 and 200 Hz on a Nvidia GeForce GTX 1060 GPU.

随着诸如移动设备和便携式深度照相机之类的硬件的发展，以深度流作为输入的在移动设备上的在线3D重建成为可能并且是有希望的。现有的大多数系统都使用体积表示法来融合深度图像，并使用ICP算法来估计摄像机的姿态。但是，ICP跟踪器在几何信息不足的场景中会发生较大的漂移。为了解决这个问题，我们提出了一种基于稳定性的采样方法，根据其几何稳定性在不同的窗口中选择不同数量的点对。此外，我们通过分析条件编号将ICP跟踪器与IMU信息融合在一起。然后，我们将基于稳定性的采样方法应用于空间哈希的体积表示。对跟踪精度和3D重建结果的定性和定量评估表明，我们的方法优于当前的最新系统，尤其是在缺少足够几何信息的场景中。总的来说，我们的方法在Apple iPad Air 2上达到20 Hz的帧频，在Nvidia GeForce GTX 1060 GPU上达到200 Hz的帧频。