Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Content creation for autostereoscopic display is a widely unresolved task. Typical methods rely on view synthesis based on depth image based rendering. Our method applies purely image domain warping instead. Input video is analyzed and information about sparse disparity, vertical edges and saliency is extracted. A constrained energy minimization problem is formulated and efficiently solved. The resulting image warping functions are used to synthesize novel views. Our approach is fully automatic, accurate, and reliable. Disocclusions and related artifacts are avoided due to smooth, saliency-driven warping functions. Our method also works ll for extrapolation of views in a limited range, thus supporting multiview creation from stereo input, which is the most relevant use case scenario
['Miquel A. Farre', 'Oliver Wang', 'Manuel Lang', 'Nikolce Stefanoski', 'Alexander Hornung', 'Aljoscha Smolic']
I am writing a research paper on a idea Automatic content creation for multiview autostereoscopic displays using image domain warping
[{"from": "human", "value": "I am writing a research paper on a idea Automatic content creation for multiview autostereoscopic displays using image domain warping"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Content creation for autostereoscopic display is a widely unresolved task. Typical methods rely on view synthesis based on depth image based rendering. Our method applies purely image domain warping instead. Input video is analyzed and information about sparse disparity, vertical edges and saliency is extracted. A constrained energy minimization problem is formulated and efficiently solved. The resulting image warping functions are used to synthesize novel views. Our approach is fully automatic, accurate, and reliable. Disocclusions and related artifacts are avoided due to smooth, saliency-driven warping functions. Our method also works ll for extrapolation of views in a limited range, thus supporting multiview creation from stereo input, which is the most relevant use case scenario"}]