The twenty-six papers in this special issue focus on Red Blue Green (RBG)-D vision, an emerging research topic in computer vision, with a number of applications in robotics, entertainment, biometrics and multimedia. Compared to 2D images and 3D data (including depth images, point clouds and meshes), RGB-D images represent both the photometric and geometric information of a scene. Moreover, low-cost consumer depth cameras (e.g., Microsoft Kinect v2, Intel Realsense, Orbbec Astra) can enable realtime applications due to their high acquisition frame-rate. In the last few years, a large number of RGB-D datasets have also been publicly released to tackle various vision tasks. Although remarkable progress has been achieved, several critical problems still remain open. The aim of this special issue is to stimulate researchers from different fields to present their state-of-the-art work, and to provide a cross-fertilization ground for discussions on the next steps in this important research area.

中文翻译：

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客座编辑对RGB-D视觉特刊的介绍：方法和应用

本期特刊中的26篇论文重点介绍了红蓝绿色（RBG）-D视觉，这是计算机视觉中一个新兴的研究主题，在机器人技术，娱乐，生物识别和多媒体中有许多应用。与2D图像和3D数据（包括深度图像，点云和网格）相比，RGB-D图像代表场景的光度和几何信息。此外，低成本的消费深度相机（例如Microsoft Kinect v2，Intel Realsense，Orbbec Astra）由于具有高采集帧率，因此可以实现实时应用。在过去的几年中，大量的RGB-D数据集也已公开发布，以解决各种视觉任务。尽管已经取得了显着进展，但仍然存在一些关键问题。