Abstract
The paper is focused on the problem of estimating 3D structure and motion of nonrigid object from a monocular video sequence. Many previous methods on this problem utilize the extension technique of factorization based on rank constraint to the tracking matrix, where the 3D shape of nonrigid object is expressed as weighted combination of a set of shape bases. All these solutions are based on the assumption of affine camera model. This assumption will become invalid and cause large reconstruction errors when the object is close to the camera. The main contribution of this paper is that we extend these methods to the general perspective camera model. The proposed algorithm iteratively updates the shape and motion from weak perspective projection to fully perspective projection by refining the scalars corresponding to the projective depths. Extensive experiments on real sequences validate the effectiveness and improvements of the proposed method.
The work is partly supported by the National Natural Science Foundation of China.
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Wang, G., Tian, Y., Sun, G. (2005). Modelling Nonrigid Object from Video Sequence Under Perspective Projection. In: Tao, J., Tan, T., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2005. Lecture Notes in Computer Science, vol 3784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573548_9
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DOI: https://doi.org/10.1007/11573548_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29621-8
Online ISBN: 978-3-540-32273-3
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