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21#
發(fā)表于 2025-3-25 05:11:17 | 只看該作者
22#
發(fā)表于 2025-3-25 08:36:01 | 只看該作者
23#
發(fā)表于 2025-3-25 15:10:36 | 只看該作者
Rotation Constrained Power Factorization,ization (RCPF) algorithm that integrates orthonormality and replicated block structure of the motion matrix directly into iterations. The algorithm is easy to implement and can work with incomplete tracking matrix. Based on the shape bases recovered by the batch-type factorization, we introduce a se
24#
發(fā)表于 2025-3-25 19:11:22 | 只看該作者
25#
發(fā)表于 2025-3-25 21:13:21 | 只看該作者
Quasi-Perspective Factorization,approximation is widely adopted due to its simplicity, whereas the extension to perspective model suffers from difficulties in projective depth recovery. To fill the gap between simplicity of affine and accuracy of perspective model, we propose a quasi-perspective factorization algorithm for structu
26#
發(fā)表于 2025-3-26 02:15:23 | 只看該作者
27#
發(fā)表于 2025-3-26 05:30:30 | 只看該作者
28#
發(fā)表于 2025-3-26 12:29:28 | 只看該作者
Less is More: Sovereignty in Europei)?Quasi-perspective projection matrix has nine degrees of freedom, and the parallelism along. and. directions in world system are preserved in images. (ii)?Quasi-fundamental matrix can be simplified to a special form with only six degrees of freedom. The fundamental matrix is invariant to any non-s
29#
發(fā)表于 2025-3-26 14:05:08 | 只看該作者
https://doi.org/10.1007/978-981-16-0757-8hen, present the main idea of the following factorization algorithms for different kinds of scenarios under different projection models. (i)?Structure and motion factorization of rigid objects under affine assumption and its extension to perspective camera model; (ii)?Nonrigid factorization under bo
30#
發(fā)表于 2025-3-26 17:36:20 | 只看該作者
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