软分割约束边缘保持插值的半自动2D转3D

半自动2D转3D将用户标注的稀疏深度转换成稠密深度,是解决3D片源不足的主要手段之一.针对现有方法利用硬分割增强深度边缘引入误差的问题,提出像素点与超像素深度一致性约束的边缘保持插值方法.首先,建立像素点深度和超像素深度传播的能量模型,通过像素点与所属超像素间深度差异的约束项将二者关联起来;其次,利用矩阵表示形式将两个能量模型的最优化转换成一个稀疏线性方程组的求解问题.通过超像素提供的约束项,可避免深度传播穿过低对比度边缘区域,从而能保持对象边缘.实验结果表明,本文方法对象边缘处深度恢复的准确性优于融合图割的随机游走方法,PSNR改善了1.5dB以上.

Semi-Automatic 2D-to-3D Conversion Using Soft Segmentation Constrained Edge-Aware Interpolation

Semi-automatic 2D-to-3D conversion is a promising solution to 3D stereoscopic content creation.Its main process is to estimate the dense depth map from user-defined strokes on the image.Existing methods preserve depth boundaries by incorporating hard segmentation.However,the inexact segmentation around object boundaries will decrease depth accuracy around these regions.To help solve this problem,an edge-aware interpolation method is developed which is constrained by depth consistency between pixels and superpixels.First,we formulate depth propagation in terms of two energy functions of pixels and superpixels,which are influenced by each other through the constraint of soft segmentation.Second,the energy functions are reformulated in matrix forms and they are solved jointly in a sparse linear equation.We recover depth boundaries with help of the superpixels constraint which prevents depth propagation across low contrast edge regions.Experimental comparisons with existing algorithms show that our method demonstrates significant advantages over object boundaries.The PSNR is improved by more than 1.5 dB compared with hybrid graph-cuts and random-walks approach.