Guest Editors' Introduction to the Special Issue on RGB-D Vision: Methods and Applications

Mohammed Bennamoun, Yulan Guo, Federico Tombari, Kamal Youcef-Toumi, Ko Nishino
2020 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Ç R GB-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 realtime applications due to their high acquisition frame-rate. In
more » ... 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. As guest editors of this special issue on "RGB-D Vision: Methods and Applications", we were happy to receive 76 submissions to our special issue. After a rigorous review process, we accepted 26 papers for publication. We thank the reviewers who provided detailed, insightful, and timely reviews, leading to the high quality of accepted papers. We would like to thank TPAMI EiC Sven Dickinson for making this special issue possible, TPAMI AE Ko Nishno for his very strong support. We are also grateful to the editorial staff for managing the submission process and providing helpful assistance. The 26 accepted papers of this special issue can be grouped into six different main categories: (i) new sensing technologies, (ii) depth estimation and enhancement, (iii) simultaneous localization and mapping (SLAM), (iv) reconstruction, (v) recognition, and (vi) scene understanding. M. Bennamoun is with the
doi:10.1109/tpami.2020.2976227 fatcat:dqt5dt3ymnesfikmgu2sxffdcu