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Fast Projective Reconstruction: Toward Ultimate Efficiency Fast Projective Reconstruction: Toward Ultimate Efficiency Hanno, Ackermann Kenichi, Kanatani Hanno, Ackermann Kenichi, Kanatani 研究論文（推薦） We accelerate the time-consuming iterations for projective reconstruction a key component of self-calibration for computing 3-D shapes from feature point tracking over a video sequence.We first summarize the algorithms of the primal and dual methods for projective reconstruction.Then we replace the eigenvalue computation in each step by the power method. We also accelerate the power method itself. Furthermore we introduce the SOR method for accelerating the subspace fitting involved in the iterations. Using simulated and real video images we demonstrate that the computation sometimes becomes several thousand times faster. We accelerate the time-consuming iterations for projective reconstruction, a key component of self-calibration for computing 3-D shapes from feature point tracking over a video sequence.We first summarize the algorithms of the primal and dual methods for projective reconstruction.Then, we replace the eigenvalue computation in each step by the power method. We also accelerate the power method itself. Furthermore, we introduce the SOR method for accelerating the subspace fitting involved in the iterations. Using simulated and real video images,we demonstrate that the computation sometimes becomes several thousand times faster. journal article 情報処理学会 2008-03-15 application/pdf 情報処理学会論文誌コンピュータビジョンとイメージメディア（CVIM） SIG6(CVIM20) 49 68 78 1882-7810 AA11560603 https://ipsj.ixsq.nii.ac.jp/record/17934/files/IPSJ-TCVIM4906009.pdf eng