Pushing the limit of non-rigid structure-from-motion by shape clustering
Recovering both camera motions and non-rigid 3D shapes from 2D feature tracks is a challenging problem in computer vision. Long-term, complex non-rigid shape variations in real world videos further increase the difficulty for Non-rigid structure-from-motion (NRSfM). Furthermore, there does not exist a criterion to characterize the possibility in recovering the non-rigid shapes and camera motions (i.e., how easy or how difficult the problem could be). In this paper, we first present an analysis...[Show more]
|Collections||ANU Research Publications|
|Book Title:||2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)|
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