A new method to create depth information based on lighting analysis for 2D/3D conversion

Han, Hyunho; Lee, Gangseong; Lee, Jongyong; Lee, Sang-Hun

doi:10.1007/s11771-013-1788-0

Cited by 2 publications

(1 citation statement)

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“…Automatic methods try to create depth from 2D images using various depth cues, such as dark channel [2], motion [3], lighting bias [4], defocus [5], geometry [6], boundary [7], etc. Each cue is only applicable to certain scenes [8], and thus, these methods are hard to provide acceptable results in any general content.…”

Section: Introductionmentioning

confidence: 99%

Robust semi-automatic 2D-to-3D image conversion via residual-driven optimization

Yuan

2018

J Image Video Proc.

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Semi-automatic 2D-to-3D conversion provides a cost-effective solution to the problem of 3D content shortage. The performance of most methods degrades significantly when cross-boundary scribbles are present due to their inability to remove unwanted input. To address this problem, a residual-driven energy function is proposed to remove unwanted input introduced by cross-boundary scribbles while preserving expected user input. Firstly, confidence of user input is computed from residuals between the estimation and user-specified depth values, and it is applied to the data fidelity term. Secondly, the residual-driven optimization is performed to estimate dense depth from user scribbles. The procedure is repeated until a maximum number of iterations is exceeded. Input confidence based on residuals avoids the propagation of unwanted scribbles and thus enables to generate high-quality depth even with cross-boundary input. Experimental results demonstrate that the proposed method removes unwanted scribbles successfully while preserving expected input, and it outperforms the state-of-the-art when presented with cross-boundary scribbles.

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