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首页> 外文期刊>International Journal of Computer Vision >Linearized alternating direction method with adaptive penalty and warm starts for fast solving transform invariant low-rank textures
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Linearized alternating direction method with adaptive penalty and warm starts for fast solving transform invariant low-rank textures

机译:具有自适应惩罚和热启动的线性化交替方向方法,用于快速求解变换不变的低秩纹理

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摘要

Transform invariant low-rank textures (TILT) is a novel and powerful tool that can effectively rectify a rich class of low-rank textures in 3D scenes from 2D images despite significant deformation and corruption. The existing algorithm for solving TILT is based on the alternating direction method. It suffers from high computational cost and is not theoretically guaranteed to converge to a correct solution to the inner loop. In this paper, we propose a novel algorithm to speed up solving TILT, with guaranteed convergence for the inner loop. Our method is based on the recently proposed linearized alternating direction method with adaptive penalty. To further reduce computation, warm starts are also introduced to initialize the variables better and cut the cost on singular value decomposition. Extensive experimental results on both synthetic and real data demonstrate that this new algorithm works much more efficiently and robustly than the existing algorithm. It could be at least five times faster than the previous method.
机译:变换不变低秩纹理(TILT)是一种新颖而强大的工具,尽管存在明显的变形和损坏,但它仍可以从2D图像有效纠正3D场景中丰富的低秩纹理。解决TILT的现有算法基于交替方向法。它遭受了高昂的计算成本,并且从理论上不能保证收敛到对内环的正确解。在本文中,我们提出了一种新颖的算法来加快TILT的求解速度,并保证了内环的收敛性。我们的方法基于最近提出的带有自适应惩罚的线性交替方向方法。为了进一步减少计算量,还引入了热启动以更好地初始化变量并减少奇异值分解的成本。在合成和真实数据上的大量实验结果表明,该新算法比现有算法更有效,更健壮。它可能比以前的方法快至少五倍。

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