RGB-D vision is 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 real-time applications due to their acquisition frame-rate. In the last few years, a large number of RGB-D datasets has been publicly released aimed at various vision tasks. Although remarkable progress has been achieved in this area, RGB-D data still pose challenges to the computer vision community due to the typically high noise and poor quality of retrieved depth data. The need for more contributions is also motivated by a number of emerging applications in the field of robotics, augmented reality, and autonomous driving.
The aim of this special issue is to showcase state-of-the-art results and to provide a cross-fertilization ground for stimulating discussions on the next steps in the area of RGB-D vision. We welcome contributions of novel work in RGB-D vision, as well as its applications in different areas.
The topics of interest include, but are not limited to, the following RGB-D vision tasks:
The University of Western Australia, Australia
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National University of Defense Technology, China
Institute of Computing Technology, Chinese Academy of Sciences, China
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