快速高分辨率图像的立体匹配方法Effective large scale stereo matching
《Effective large scale stereo matching》
In this paper we propose a novel approach to binocular stereo for fast matching of high-resolution images. Our approach builds a prior on the disparities by forming a triangulation on a set of support points which can be robustly matched, reducing the matching ambiguities of the remaining points. This allows for efficient exploitation of the disparity search space, yielding accurate dense reconstruction without the need for global optimization.
该文章发表在2010年的accv上面,看了一下公式和代码都异常复杂,主要思想就是通过待匹配图像上的特征点作为匹配的支撑点,在支撑点上做三角剖分,对视差在进行插值计算,但是效果一般,适用于实时性要求高的场合。
整个工程都用OpenCV和openmp 完成,代码异常复杂,但是整体上非常吻合工程师的代码风格,可以学习一下,但是改进难度比较大。
效果:明显看出效果和普通的全局优化算法还是有一定的差距。
文章下载链接:
http://www.cvlibs.net/software/libelas/
http://www.cvlibs.net/publications/Geiger2010ACCV.pdf
代码下载链接:
http://www.cvlibs.net/download.php?file=libelas.zip
openmp版本:运行时间非常短大概0.1s左右完全满足实时性要求。
http://www.cvlibs.net/download.php?file=libelas_omp.zip
我调试好的工程:
http://download.csdn.net/detail/wangyaninglm/9321831