A Novel Algebaric Variety Based Model for High Quality Free-Viewpoint View Synthesis on a Krylov Subspace
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1,677 citations
"A Novel Algebaric Variety Based Mod..." refers background or methods in this paper
...On Microsoft Research 3D videos, we synthesized camera 4 as a novel viewpoint using reference color and depth images....
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...The color and associated depth images are provided for each camera along with the calibration information [23]....
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...The proposed algorithm is tested on benchmark datasets: Middlebury stereo data [22] and multi-view “Breakdancing” and “Ballet” 3D Video data provided by Microsoft Research [23]....
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...Microsoft Research multi-view video-plus-depth data includes a sequence of 100 frames acquired from eight synchronized cameras....
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1,164 citations
"A Novel Algebaric Variety Based Mod..." refers methods in this paper
...The proposed algorithm is tested on benchmark datasets: Middlebury stereo data [22] and multi-view “Breakdancing” and “Ballet” 3D Video data provided by Microsoft Research [23]....
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1,068 citations
"A Novel Algebaric Variety Based Mod..." refers background or methods in this paper
...[24] inpainting or plane fitting approaches for hole filling in their intermediate procedures....
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...[4, 5] rely on existing inpainting technique [24] or plane fitting approach to recover the missing marked pixels....
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...[24] rely on filling missing gaps propagating isophote lines continuously using information from the surrounding area....
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363 citations
"A Novel Algebaric Variety Based Mod..." refers methods in this paper
...[30] K. Mohan and M. Fazel....
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...The simplest case of p = 1 is considered to recover nuclear norm, which is re-expressed as a weighted Frobenius norm: ||Φd(HK{xj})||∗ = tr[((Φd(HK{xj})) TΦd(HK{xj})) 1/2] = tr[(Φd(HK{xj})) TΦd(HK{xj})... ((Φd(HK{xj})) TΦd(HK{xj})) −1/2] (9) The nuclear norm of the data matrix Φd(HK{xj}) is minimized by performing the IRLS iterations as Wn = (k(HK{xj}, HK{xj}) + γnI) −1/2, (HK{xj})n+1 = argmin HK{xj} tr[k(HK{xj}, HK{xj})Wn], PΩ(HK{xj}) = PΩ(H 0 K{xj}) (10) The projected gradient procedure as suggested by Mohan and Fazel [30] is applied to find the exact minimum in the update of Φd(HK{xj} (H̃K{xj})n = (HK{xj})n− τ(HK{xj})n(W ⊙ kd−1((HK{xj})n, (HK{xj})n)) (HK{xj})n = PΩ((HK{xj})0) + PΩc((H̃K{xj})) (11) where k denotes the kernel matrix, ⊙ denotes an entry-wise product, τ is a step-size parameter....
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...The projected gradient procedure as suggested by Mohan and Fazel [30] is applied to find the exact minimum in the update of Φd(HK{xj}...
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...We accommodated the non-convex Schatten-p relaxation of the rank penalty, in addition to the convex nuclear norm relaxation in iterative reweighted least squares [30] and solved the following optimization problem on the Krylov space...
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323 citations
"A Novel Algebaric Variety Based Mod..." refers background or methods in this paper
...[7] applied median filter followed by bilateral filter to recover missing pixels in the warped depth maps....
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...[7] approach, background information is lost due to Z-buffering because disocclusion filling operation is performed on the virtual image....
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...[7] approaches cannot compete with Sharma et al....
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...The artifacts like holes or cracks caused by resampling or disocclusion and ghost contours caused by ill-defined borders or discontinuities in depth maps, are mainly associated with 3D warping technique [4, 5, 6, 7]....
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...[7] describe challenges of 3D warping and suggested some approaches to improve the standard DIBR pipeline....
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