基于投影散斑的实时场景深度恢复
Real-Time Depth Recovery Based on Projected Speckles
-
摘要: 为了解决传统匹配算法重建散斑图像时在视差不连续区域误匹配较多的问题,提出一种在网格顶点处选择种子点、渐进增长的区域生长匹配方法,以完成散斑图像的深度信息恢复.针对传统区域生长方法种子点选取速度低、覆盖性差的问题,提出在网格顶点处选择种子点的快速定位方法;在生长策略方面,采用逐步放宽种子点选择标准和生长标准的方式,渐进地增长区域;为克服散斑图像亮度不均衡的问题,采用零均值归一化互相关算子作为相关测度算子,并针对该算子冗余计算较多的问题,运用积分图结合其改进计算公式加速计算,确保算法的实时性;最后插值细化视差图,并根据三角测量原理转化视差值为深度值.实验结果表明,该方法深度恢复结果鲁棒性强、速度较快.Abstract: In order to solve the problem that many mismatching points exist when recovering speckle images by means of traditional matching algorithms,a region growing matching scheme characterized by selecting seed points at grid vertices and growing progressively is proposed.In order to address the problem of low speed and omission of certain regions,a fast method to select seed points at grid vertices is proposed.In terms of growing strategy,this paper stepwise loosens the criteria of seed points selection and region growing to progressively grow regions.To overcome the influence of uneven-brightness of speckle images,zero-mean normalized cross correlation method is adopted as the matching operator.In order to avoid the redundant calculation of zero-mean normalized cross correlation method,integral image and the more computationally efficient expression of zero-mean normalized cross correlation method are combined to speed the calculation and ensure the real-time performance.Finally,we interpolate the disparity image and transform the disparity to depth according to the triangulation theory.The experimental results show that,the proposed approach can recover the depth information of speckle images fast and robustly.