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|Title:||"Maximizing rigidity" revisited: a convex programming approach for generic 3D shape reconstruction from multiple perspective views|
|Citation:||Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017), 2017 / vol.2017-October, pp.929-937|
|Publisher Place:||Piscataway, NJ|
|Series/Report no.:||IEEE International Conference on Computer Vision|
|Conference Name:||IEEE International Conference on Computer Vision (ICCV 2017) (22 Oct 2017 - 29 Oct 2017 : Venice, ITALY)|
|Pan Ji, Hongdong Li, Yuchao Dai, Ian Reid|
|Abstract:||Rigid structure-from-motion (RSfM) and non-rigid structure-from-motion (NRSfM) have long been treated in the literature as separate (different) problems. Inspired by a previous work which solved directly for 3D scene structure by factoring the relative camera poses out, we revisit the principle of “maximizing rigidity” in structure-from-motion literature, and develop a unified theory which is applicable to both rigid and non-rigid structure reconstruction in a rigidity-agnostic way. We formulate these problems as a convex semi-definite program, imposing constraints that seek to apply the principle of minimizing non-rigidity. Our results demonstrate the efficacy of the approach, with stateof- the-art accuracy on various 3D reconstruction problems.|
|Rights:||© 2017 IEEE|
|Appears in Collections:||Computer Science publications|
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