An efficient approach for 2D to 3D video conversion based on structure from motion
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| 003 | CHVBK | ||
| 005 | 20210128100910.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20150101xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00371-013-0904-3 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00371-013-0904-3 | ||
| 245 | 0 | 3 | |a An efficient approach for 2D to 3D video conversion based on structure from motion |h [Elektronische Daten] |c [Wei Liu, Yihong Wu, Fusheng Guo, Zhanyi Hu] |
| 520 | 3 | |a 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. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2013 | ||
| 690 | 7 | |a 2D to 3D conversion |2 nationallicence | |
| 690 | 7 | |a Structure from motion |2 nationallicence | |
| 690 | 7 | |a Depth warping |2 nationallicence | |
| 690 | 7 | |a Depth map |2 nationallicence | |
| 700 | 1 | |a Liu |D Wei |u Center for Internet of Things, Institute of Microelectronics of Chinese Academy of Sciences, 100029, Beijing, China |4 aut | |
| 700 | 1 | |a Wu |D Yihong |u National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, 100190, Beijing, China |4 aut | |
| 700 | 1 | |a Guo |D Fusheng |u National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, 100190, Beijing, China |4 aut | |
| 700 | 1 | |a Hu |D Zhanyi |u National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, 100190, Beijing, China |4 aut | |
| 773 | 0 | |t The Visual Computer |d Springer Berlin Heidelberg |g 31/1(2015-01-01), 55-68 |x 0178-2789 |q 31:1<55 |1 2015 |2 31 |o 371 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00371-013-0904-3 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00371-013-0904-3 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Liu |D Wei |u Center for Internet of Things, Institute of Microelectronics of Chinese Academy of Sciences, 100029, Beijing, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Wu |D Yihong |u National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, 100190, Beijing, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Guo |D Fusheng |u National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, 100190, Beijing, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Hu |D Zhanyi |u National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, 100190, Beijing, China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t The Visual Computer |d Springer Berlin Heidelberg |g 31/1(2015-01-01), 55-68 |x 0178-2789 |q 31:1<55 |1 2015 |2 31 |o 371 | ||