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Three-Dimensional Traffic Scenes Simulation From Road Image Sequences

机译:基于道路图像序列的三维交通场景模拟

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In this paper, we present a novel framework to allow users to tour simulated traffic scenes from the first-person view. Constructing 3-D scenes from road image sequences is in general difficult, due to the intrinsic complexity of dynamic road scenes, which are composed of a drastically moving background, not to mention numerous other surrounding vehicles. With the definitions of the traffic scene models, we first introduce the construction process of the simple traffic scenes. After the detection of road boundaries by a semantic fast two-cycle (FTC) level set method, we generate the control points on road sides to construct the “floor-wall” background scene that is subsequently propagated to each frame. Furthermore, we approach the cluttered traffic scenes through a three-component processing pipeline as follows: 1) traffic elements segmentation; 2) background images inpainting; and 3) traffic scenes construction. The traffic elements in the cluttered images are segmented by the semantic FTC level set method first. A Gaussian mixture model is then employed to inpaint the occluded background utilizing the optical flows. The cluttered traffic scenes can be constructed after the segmentation and inpainting components. The foreground polygons such as vehicles and traffic signs are then modeled. Users can change their viewpoints according to their own interpretations. We present the evaluations of each technical component, followed by our findings from comprehensive user studies, which well demonstrate the effectiveness of the proposed framework in delivering good touring experience to users.
机译:在本文中,我们提出了一个新颖的框架,允许用户从第一人称视角浏览模拟的交通场景。由于动态道路场景的内在复杂性,动态道路场景的内在复杂性通常由动态运动的背景组成,更不用说其他众多周围的车辆了,从道路图像序列构造3D场景通常很困难。利用交通场景模型的定义,我们首先介绍简单交通场景的构建过程。在通过语义快速两周期(FTC)水平集方法检测到道路边界后,我们在道路两侧生成控制点以构建“地板”背景场景,随后将其传播到每个帧。此外,我们通过三部分处理流水线来处理混乱的交通场景,如下所示:1)交通要素分割; 2)背景图片修复; 3)交通场景建设。首先,通过语义FTC级别设置方法对混乱图像中的交通要素进行分割。然后,采用高斯混合模型,利用光流对被遮挡的背景进行修补。可以在分割和修复组件之后构建凌乱的交通场景。然后对前景多边形(例如车辆和交通标志)进行建模。用户可以根据自己的解释改变观点。我们将介绍每个技术组件的评估,然后是我们从全面的用户研究中得出的结论,这些结果充分证明了所提出的框架在向用户提供良好的旅游体验方面的有效性。

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