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|Title:||3D reconstruction of dynamic scenes with multiple handheld cameras|
|Citation:||Jiang, H.,Liu, H.,Tan, P.,Zhang, G.,Bao, H. (2012). 3D reconstruction of dynamic scenes with multiple handheld cameras. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7573 LNCS (PART 2) : 601-615. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-33709-3_43|
|Abstract:||Accurate dense 3D reconstruction of dynamic scenes from natural images is still very challenging. Most previous methods rely on a large number of fixed cameras to obtain good results. Some of these methods further require separation of static and dynamic points, which are usually restricted to scenes with known background. We propose a novel dense depth estimation method which can automatically recover accurate and consistent depth maps from the synchronized video sequences taken by a few handheld cameras. Unlike fixed camera arrays, our data capturing setup is much more flexible and easier to use. Our algorithm simultaneously solves bilayer segmentation and depth estimation in a unified energy minimization framework, which combines different spatio-temporal constraints for effective depth optimization and segmentation of static and dynamic points. A variety of examples demonstrate the effectiveness of the proposed framework. © 2012 Springer-Verlag.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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