caa a22 4500 605540195 CHVBK 20210128100910.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00371-013-0904-3 doi (NATIONALLICENCE)springer-10.1007/s00371-013-0904-3 An efficient approach for 2D to 3D video conversion based on structure from motion [Elektronische Daten] [Wei Liu, Yihong Wu, Fusheng Guo, Zhanyi Hu] With the popularity of 3D films, the conversion of existing 2D videos to 3D videos has attracted a wide interest in 3D content production. In this paper, we present an efficient approach for 2D to 3D video conversion based on structure from motion (SFM). The key contributions include a piece-wise SFM approach and a novel nonlinear depth warping considering the characteristics of stereoscopic 3D. The dense depth maps are generated and further refined with color segmentation. Experiments show that the proposed approach can yield more visually satisfactory results. Springer-Verlag Berlin Heidelberg, 2013 2D to 3D conversion nationallicence Structure from motion nationallicence Depth warping nationallicence Depth map nationallicence Liu Wei Center for Internet of Things, Institute of Microelectronics of Chinese Academy of Sciences, 100029, Beijing, China aut Wu Yihong National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, 100190, Beijing, China aut Guo Fusheng National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, 100190, Beijing, China aut Hu Zhanyi National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, 100190, Beijing, China aut The Visual Computer Springer Berlin Heidelberg 31/1(2015-01-01), 55-68 0178-2789 31:1<55 2015 31 371 https://doi.org/10.1007/s00371-013-0904-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00371-013-0904-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Liu Wei Center for Internet of Things, Institute of Microelectronics of Chinese Academy of Sciences, 100029, Beijing, China aut NATIONALLICENCE 700 1- Wu Yihong National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, 100190, Beijing, China aut NATIONALLICENCE 700 1- Guo Fusheng National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, 100190, Beijing, China aut NATIONALLICENCE 700 1- Hu Zhanyi National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, 100190, Beijing, China aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/1(2015-01-01), 55-68 0178-2789 31:1<55 2015 31 371