Defogging Kinect: Simultaneous Estimation of Object Region and Depth in Foggy Scenes
Defogging Kinect: Simultaneous Estimation of Object Region and Depth in Foggy Scenes
复制标题
DOI：
--
复制
发表时间：
2019-04
期刊：
影响因子：
--
通讯作者：
Yuki Fujimura;Motoharu Sonogashira;M. Iiyama
中科院分区：
文献类型：
--
作者：
Yuki Fujimura;Motoharu Sonogashira;M. Iiyama
从二维图像中进行三维重建和场景深度估计是计算机视觉中的主要任务。然而，使用传统的3D重建技术在参与的介质中（如浑浊的水，雾或烟雾）具有挑战性。我们已经开发了一种方法，使用飞行时间（ToF）相机来估计对象区域和深度同时在参与媒体。散射分量是饱和的，因此它不依赖于场景深度，并且由于参与介质中的光衰减，从远处点反射的接收信号可以忽略不计，因此对这样的点的观测仅包含散射分量。这些现象使我们能够从只包含散射分量的背景中估计目标区域中的散射分量。该问题被制定为鲁棒估计的对象区域被视为离群值，它使同时估计的对象区域和深度的基础上的迭代加权最小二乘（IRLS）优化方案。我们证明了所提出的方法的有效性，使用捕获的图像从Kinect v2在真实的有雾的场景，并评估与合成数据的适用性。
Three-dimensional (3D) reconstruction and scene depth estimation from 2-dimensional (2D) images are major tasks in computer vision. However, using conventional 3D reconstruction techniques gets challenging in participating media such as murky water, fog, or smoke. We have developed a method that uses a time-of-flight (ToF) camera to estimate an object region and depth in participating media simultaneously. The scattering component is saturated, so it does not depend on the scene depth, and received signals bouncing off distant points are negligible due to light attenuation in the participating media, so the observation of such a point contains only a scattering component. These phenomena enable us to estimate the scattering component in an object region from a background that only contains the scattering component. The problem is formulated as robust estimation where the object region is regarded as outliers, and it enables the simultaneous estimation of an object region and depth on the basis of an iteratively reweighted least squares (IRLS) optimization scheme. We demonstrate the effectiveness of the proposed method using captured images from a Kinect v2 in real foggy scenes and evaluate the applicability with synthesized data.