DocumentCode
128720
Title
Benchmark datasets for 3D computer vision
Author
Yulan Guo ; Jun Zhang ; Min Lu ; Jianwei Wan ; Yanxin Ma
Author_Institution
Coll. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol., Changsha, China
fYear
2014
fDate
9-11 June 2014
Firstpage
1846
Lastpage
1851
Abstract
With the rapid development of range image acquisition techniques, 3D computer vision has became a popular research area. It has numerous applications in various domains including robotics, biometrics, remote sensing, entertainment, civil construction, and medical treatment. Recently, a large number of algorithms have been proposed to address specific problems in the area of 3D computer vision. Meanwhile, several benchmark datasets have also been released to stimulate the research in this area. The availability of benchmark datasets plays an significant role in the process of technological progress. In this paper, we first introduce several major 3D acquisition techniques. We also present an overview on various popular topics in 3D computer vision including 3D object modeling, 3D model retrieval, 3D object recognition, 3D face recognition, RGB-D vision, and 3D remote sensing. Moreover, we present a contemporary summary of the existing benchmark datasets in 3D computer vision. This paper can therefore, serve as a handbook for those who are working in the related areas.
Keywords
computer vision; face recognition; image retrieval; object recognition; 3D acquisition techniques; 3D computer vision; 3D face recognition; 3D model retrieval; 3D object modeling; 3D object recognition; 3D remote sensing; RGB-D vision; benchmark datasets; biometrics; civil construction; entertainment; medical treatment; range image acquisition technique; robotics; Benchmark testing; Face; Face recognition; Mathematical model; Shape; Solid modeling; Three-dimensional displays;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on
Conference_Location
Hangzhou
Print_ISBN
978-1-4799-4316-6
Type
conf
DOI
10.1109/ICIEA.2014.6931468
Filename
6931468
Link To Document