Survey on depth and RGB image-based 3D hand shape and pose estimation

查看全文 作　　者：Lin [1]HUANG;Boshen [2]ZHANG;Zhilin [3]GUO;Yang [4]XIAO;Zhiguo [4]CAO;Junsong [1]YUAN

机构地区：[1]Department of Computer Science and Engineering,State University of New York at Buffalo,NY 14260,USA;[2]YouTu Lab,Tencent,Shanghai 201101,China;[3]Department of Computer Science,Fu Foundation School of Engineering and Applied Science,Columbia University,NY 10027,USA;[4]National Key Laboratory of Science and Technology on Multispectral Information Processing,School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China

出　　处：《Virtual Reality & Intelligent Hardware》索引2021年第3卷第3期,共28页

基　　金：the National Key R&D Program of China(2018YFB1004600);the National Natural Science Foundation of China(61502187,61876211);the National Science Foundation Grant CNS(1951952). 摘　　要：The field of vision-based human hand three-dimensional(3D)shape and pose estimation has attracted significant attention recently owing to its key role in various applications,such as natural human computer interactions.With the availability of large-scale annotated hand datasets and the rapid developments of deep neural networks(DNNs),numerous DNN-based data-driven methods have been proposed for accurate and rapid hand shape and pose estimation.Nonetheless,the existence of complicated hand articulation,depth and scale ambiguities,occlusions,and finger similarity remain challenging.In this study,we present a comprehensive survey of state-of-the-art 3D hand shape and pose estimation approaches using RGB-D cameras.Related RGB-D cameras,hand datasets,and a performance analysis are also discussed to provide a holistic view of recent achievements.We also discuss the research potential of this rapidly growing field. 关键词：Hand survey 3D hand pose estimation Hand shape reconstruction Hand-object interactions RGB-D cameras

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Survey on depth and RGB image-based 3D hand shape and pose estimation

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