图像合成与深度配准

Zan Li, Wencheng Wang, Fei Hou
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摘要

对于图像合成来说,处理遮挡仍然是一个具有挑战性的问题。它总是要求源内容完全在目标内容的前面,或者需要人工干预来调整遮挡,这是非常繁琐的。虽然有几种方法建议利用先验或学习技术来促进咬合确定,但它们的潜力非常有限。本文通过提出一种深度配准方法,将源内容无缝地合并到目标图像所代表的3D空间中,从而解决了这一挑战。因此,可以通过逐像素深度比较方便地处理源内容和目标内容之间的遮挡,使用户能够更有效地专注于图像构图的设计。实验结果表明,我们可以方便地处理图像合成中的遮挡,效率比Photoshop提高了4倍左右。
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Image Composition with Depth Registration
Handling occlusions is still a challenging problem for image composition. It always requires the source contents to be completely in front of the target contents or needs manual interventions to adjust occlusions, which is very tedious. Though several methods have suggested exploiting priors or learning techniques for promoting occlusion determination, their potentials are much limited. This paper addresses the challenge by presenting a depth registration method for merging the source contents seamlessly into the 3D space that the target image represents. Thus, the occlusions between the source contents and target contents can be conveniently handled through pixel-wise depth comparisons, allowing the user to more efficiently focus on the designs for image composition. Experimental results show that we can conveniently handle occlusions in image composition and improve efficiency by about 4 times compared to Photoshop.
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