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Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/166698
Título

Combining local-physical and global-statistical models for sequential deformable shape from motion

AutorAgudo, Antonio ; Moreno-Noguer, Francesc
Palabras claveLow-rank models
Sequential non-rigid structure from motion
Particle dynamics
Bundle adjustment
Fecha de publicación2017
EditorSpringer Nature
CitaciónInternational Journal of Computer Vision 122(2): 371-387 (2017)
ResumenIn this paper, we simultaneously estimate camera pose and non-rigid 3D shape from a monocular video, using a sequential solution that combines local and global representations. We model the object as an ensemble of particles, each ruled by the linear equation of the Newton’s second law of motion. This dynamic model is incorporated into a bundle adjustment framework, in combination with simple regularization components that ensure temporal and spatial consistency. The resulting approach allows to sequentially estimate shape and camera poses, while progressively learning a global low-rank model of the shape that is fed back into the optimization scheme, introducing thus, global constraints. The overall combination of local (physical) and global (statistical) constraints yields a solution that is both efficient and robust to several artifacts such as noisy and missing data or sudden camera motions, without requiring any training data at all. Validation is done in a variety of real application domains, including articulated and non-rigid motion, both for continuous and discontinuous shapes. Our on-line methodology yields significantly more accurate reconstructions than competing sequential approaches, being even comparable to the more computationally demanding batch methods.
Versión del editorhttps://doi.org/10.1007/s11263-016-0972-8
URIhttp://hdl.handle.net/10261/166698
Identificadoresdoi: 10.1007/s11263-016-0972-8
e-issn: 1573-1405
issn: 0920-5691
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