Survey on Supervised Learning Based Depth Estimation from a Single Image

被引:0
作者
Bi T. [1 ]
Liu Y. [1 ]
Weng D. [1 ]
Wang Y. [1 ]
机构
[1] School of Optoelectronics, Beijing Institute of Technology, Beijing
来源
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2018年 / 30卷 / 08期
关键词
3D reconstruction; Deep learning; Depth estimation; Machine learning;
D O I
10.3724/SP.J.1089.2018.16882
中图分类号
TB18 [人体工程学]; Q98 [人类学];
学科分类号
030303 ; 1201 ;
摘要
Depth estimation from a single image is an important technology in the image-based depth acquisition for 3D reconstruction, which is also a classical problem in computer vision. Recently, supervised learning based depth es-timation from a single image develops rapidly. In this paper, the recent related literatures are reviewed and super-vised learning based depth estimation from a single image and its model and optimization are introduced. The current research situations of the parametric learning method, non-parametric learning method and deep learning method both in domestic and abroad are analyzed respectively with their advantages and disadvantages. At last, summarizing these methods leads to the conclusion that depth estimation from a single image in deep learning framework is the development trend and research priority in the future. © 2018, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
引用
收藏
页码:1383 / 1393
页数:10
相关论文
共 58 条
[1]  
Herbort S., Wohler C., An introduction to image-based 3D surface reconstruction and a survey of photometric stereo methods, 3D Research, 2, 3, pp. 1-17, (2011)
[2]  
Liu W., Liu Y., Review on illumination estimation inaugmented reality, Journal of Computer-Aided Design & Computer Graphics, 28, 2, pp. 197-207, (2016)
[3]  
Xu W., Wang Y., Liu Y., Et al., Survey on occlusion handling in augmented reality, Journal of Computer Aided Design & Computer Graphics, 25, 11, pp. 1635-1642, (2013)
[4]  
Liu B.Y., Gould S., Koller D., Single image depth estimation from predicted semantic labels, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1253-1260, (2010)
[5]  
Criminisi A., Reid I., Zisserman A., Single view metrology, International Journal of Computer Vision, 40, 2, pp. 123-148, (2000)
[6]  
Barnard S.T., Fischler M.A., Computational stereo, ACM Computing Surveys, 14, 4, pp. 553-572, (1982)
[7]  
Dhond U.R., Aggarwal J.K., Structure from stereo-a review, IEEE Transactions on Systems Man & Cybernetics, 19, 6, pp. 1489-1510, (1989)
[8]  
Dellaert F., Seitz S.M., Thorpe C.E., Et al., Structure from motion without correspondence, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 557-564, (2000)
[9]  
Tomasi C., Kanade T., Shape and motion from image streams under orthography: a factorization method, International Journal of Computer Vision, 9, 2, pp. 137-154, (1992)
[10]  
Zhang R., Tsai P.S., Cryer J.E., Et al., Shape from shading: a survey, IEEE Transactions on Pattern Analysis & Machine In-telligence, 21, 8, pp. 690-706, (1999)