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The factorization method is known to be robust and efficient for the recovery of shape and motion from an image sequence by applying Singular Value Decomposition to the tracking matrix. To get all-around 3-D data of an object, the all~around view of the object must be taken as pictures. This means that a long image sequence is required, and there is almost no feature point that can be tracked throughout all frames. This occurs because of occlusion. Consequently a large tracking matrix in which most elements are unknown is acquired. It is impractical to apply the conventional factorization method directly to such a tracking matrix, because most of the elements are unknown. Instead of applying the factorization method directly to the tracking matrix, the matrix is first divided into sub-matrices having overlapping portions. After unknown elements are estimated in each sub-matrix, the factorization method is applied to each sub-matrix to recover the partial 3-D data. Then the partial 3-D data is integrated into a whole according to the overlapped portions of each pair of sub-matrices. By modifying the factorization method in this split-and-merge manner, not only can the all-around 3-D data be recovered, but also the computation time is decreased dramatically.
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