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Geodesic pixel neighborhoods for 2D and 3D scene understanding

机译：用于2D和3D场景理解的测地像素邻域

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摘要

Scene understanding is an important class of computer vision problems that is an enabler for a wide variety of applications such as advanced driver assistance systems, autonomous vehicles or mobile assistive robots. Semantic segmentation is one of the common ways to address this problem. Unlike the more standard approaches based on a probabilistic graphical model, in this paper we present a two stage classification framework based on the concept of pixel neighborhoods. In the first stage, every pixel is classified based on its appearance. The output of the first classifier in a specific region around every pixel, which we call the pixel neighborhood, is summarized by a novel voting histogram feature and given as input to a second classifier. We show how to define the pixel neighborhood by using the geodesic distance in a way that it is able to capture both local image context as well as more global object relations. We perform a quantitative and qualitative evaluation on six well-known and challenging datasets and show that our model is able to natively handle both 2D and 3D data. We compare our method to several baselines and multiple closely related methods and show state-of-the-art performance. We also present a real world application of our method in a system that automatically detects parking spaces from a moving vehicle in real time.
机译：场景理解是计算机视觉问题的重要一类，它是多种应用程序的促成因素，例如高级驾驶员辅助系统，自动驾驶汽车或移动辅助机器人。语义分割是解决此问题的常用方法之一。与基于概率图形模型的更标准方法不同，在本文中，我们提出了一种基于像素邻域概念的两阶段分类框架。在第一阶段，根据每个像素的外观对其进行分类。第一个分类器在每个像素周围特定区域的输出（我们称为像素邻域）通过新颖的投票直方图特征进行汇总，并作为第二个分类器的输入。我们展示了如何通过使用测地线距离来定义像素邻域，从而能够捕获本地图像上下文以及更多全局对象关系。我们对六个著名且具有挑战性的数据集进行了定性和定量评估，结果表明我们的模型能够本地处理2D​​和3D数据。我们将我们的方法与几个基线和多个紧密相关的方法进行比较，并显示出最新的性能。我们还介绍了我们的方法在系统中的实际应用，该系统可自动实时检测行驶中的车辆的停车位。

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