The ability to quickly and intuitively edit digital content has become increasingly important in our everyday life.However,existing edit propagation methods for editing digital images are typically based on optimizati...The ability to quickly and intuitively edit digital content has become increasingly important in our everyday life.However,existing edit propagation methods for editing digital images are typically based on optimization with high computational cost for large inputs.Moreover,existing edit propagation methods are generally inefficient and highly time-consuming.Accordingly,to improve edit efficiency,this paper proposes a novel edit propagation method using a bilateral grid,which can achieve instant propagation of sparse image edits.Firstly,given an input image with user interactions,we resample each of its pixels into a regularly sampled bilateral grid,which facilitates efficient mapping from an image to the bilateral space.As a result,all pixels with the same feature information(color,coordinates)are clustered to the same grid,which can achieve the goal of reducing both the amount of image data processing and the cost of calculation.We then reformulate the propagation as a function of the interpolation problem in bilateral space,which is solved very efficiently using radial basis functions.Experimental results show that our method improves the efficiency of color editing,making it faster than existing edit approaches,and results in excellent edited images with high quality.展开更多
针对基于深度学习的多视图立体(Multi-view Stereo,MVS)重建算法内存消耗过大、推理速度慢,以及对病态区域重建效果不佳的问题,提出了一种基于双边网格和融合代价体的轻量级级联的MVS重建网络。首先利用基于双边网格的代价体上采样模块...针对基于深度学习的多视图立体(Multi-view Stereo,MVS)重建算法内存消耗过大、推理速度慢,以及对病态区域重建效果不佳的问题,提出了一种基于双边网格和融合代价体的轻量级级联的MVS重建网络。首先利用基于双边网格的代价体上采样模块将较低分辨率代价体高效地恢复成高分辨率代价体。随着采用轻量级的动态区域卷积和粗粒度代价体融合模块,提升网络对病态区域特征的表示能力以及对场景整体信息和结构信息的感知能力。实验结果表明,该网络在DTU数据集以及Tanks and Temples数据集上均取得了具有竞争性的结果,并且在内存消耗以及推理速度上都显著优于其他方法。展开更多
基金supported by National Natural Science Foundation of China(No.U1836208,No.61402053 and No.61202439)Natural Science Foundation of Hunan Province of China(No.2019JJ50666 and No.2019JJ50655)partly supported by Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems(Changsha University of Science&Technology)(No.KFJ180701).
文摘The ability to quickly and intuitively edit digital content has become increasingly important in our everyday life.However,existing edit propagation methods for editing digital images are typically based on optimization with high computational cost for large inputs.Moreover,existing edit propagation methods are generally inefficient and highly time-consuming.Accordingly,to improve edit efficiency,this paper proposes a novel edit propagation method using a bilateral grid,which can achieve instant propagation of sparse image edits.Firstly,given an input image with user interactions,we resample each of its pixels into a regularly sampled bilateral grid,which facilitates efficient mapping from an image to the bilateral space.As a result,all pixels with the same feature information(color,coordinates)are clustered to the same grid,which can achieve the goal of reducing both the amount of image data processing and the cost of calculation.We then reformulate the propagation as a function of the interpolation problem in bilateral space,which is solved very efficiently using radial basis functions.Experimental results show that our method improves the efficiency of color editing,making it faster than existing edit approaches,and results in excellent edited images with high quality.
文摘针对基于深度学习的多视图立体(Multi-view Stereo,MVS)重建算法内存消耗过大、推理速度慢,以及对病态区域重建效果不佳的问题,提出了一种基于双边网格和融合代价体的轻量级级联的MVS重建网络。首先利用基于双边网格的代价体上采样模块将较低分辨率代价体高效地恢复成高分辨率代价体。随着采用轻量级的动态区域卷积和粗粒度代价体融合模块,提升网络对病态区域特征的表示能力以及对场景整体信息和结构信息的感知能力。实验结果表明,该网络在DTU数据集以及Tanks and Temples数据集上均取得了具有竞争性的结果,并且在内存消耗以及推理速度上都显著优于其他方法。
