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孙强强, 兰诚栋, 陈康杰, 方大锐. 全景纵向漫游中极线匹配的置信传播算法[J]. 计算机辅助设计与图形学学报, 2018, 30(3): 400-407. DOI: 10.3724/SP.J.1089.2018.16385
引用本文: 孙强强, 兰诚栋, 陈康杰, 方大锐. 全景纵向漫游中极线匹配的置信传播算法[J]. 计算机辅助设计与图形学学报, 2018, 30(3): 400-407. DOI: 10.3724/SP.J.1089.2018.16385
Sun Qiangqiang, Lan Chengdong, Chen Kangjie, Fang Darui. Belief Propagation Algorithm for Epipolar Matching in Panoramic Longitudinal Roamin[J]. Journal of Computer-Aided Design & Computer Graphics, 2018, 30(3): 400-407. DOI: 10.3724/SP.J.1089.2018.16385
Citation: Sun Qiangqiang, Lan Chengdong, Chen Kangjie, Fang Darui. Belief Propagation Algorithm for Epipolar Matching in Panoramic Longitudinal Roamin[J]. Journal of Computer-Aided Design & Computer Graphics, 2018, 30(3): 400-407. DOI: 10.3724/SP.J.1089.2018.16385

全景纵向漫游中极线匹配的置信传播算法

Belief Propagation Algorithm for Epipolar Matching in Panoramic Longitudinal Roamin

  • 摘要: 深度信息的获取是全景纵向漫游的关键基础,为了提高前后场景图像匹配的精确度,提出一种极线匹配的置信传播算法.首先根据对极几何原理,以图像中心点为基准全方向发散构建出前后场景图像的对极线;其次利用对极线路径信息在匹配代价函数中增加垂直方向的匹配代价分量,并用置信传播算法生成视差图;最后通过前后场景图像的几何关系构建视差与深度的计算模型,从而获取深度信息.实验结果表明,与局部优化算法相比,该算法深度图的结构相似性平均提高了0.22,峰值信噪比平均提高了24%,对于前后场景图像深度信息的获取具有更好的效果.

     

    Abstract: Depth information acquisition is the key foundation of panoramic longitudinal roaming.In order to improve the accuracy of before and after scene images matching,the belief propagation algorithm for the epipolar matching is proposed.According to the principle of epipolar geometry,the epipolar line of scene images is constructed by taking the image center as the benchmark and diverging in the whole direction.The vertical matching cost component is added to the matching cost function,and the disparity map is generated by the belief propagation algorithm.Through the geometric relationship between the scene images,the model of disparity and depth is constructed to obtain the depth information.Experimental results show that,compared with the local optimization algorithms,this algorithm is better for obtaining the depth map of scene images.SSIM and PSNR are improved by 0.22 and 24%respectively.

     

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