摘要
传统双目三维重建对纹理信息不清晰的目标物体存在误匹配率高、重建点云稀疏等问题。为此,提出一种新的散斑三维重建方法。将数字散斑和基于贝叶斯(Bayes)理论的稠密匹配算法相结合,对纹理匮乏的物体实现高精度稠密重建。介绍基于Bayes立体匹配与数字散斑相结合进行三维重建的原理,证明两种方法结合使用的可行性,并对多对双目组成的多测量头系统进行标定,将多测量头获得的点云拼接,形成物体的完整轮廓。实验结果表明,在物体距离多测量头系统500 mm时,采用Bayes立体匹配算法的三维重建匹配精度可达到0.08 mm,在不损耗重建效率的情况下,点云数量和点云精度都有明显提升。
The traditional binocular 3D reconstruction methods sparse ,high false match rates for the target with poor texture reconstruction method. It combines the the digital speckle and have several disadvantages such as the rebuild point cloud information and so on. This paper proposes a novel 3D dense matching algorithm based on Bayes theory, which achieves high-precision dense reconstruction for texture-deficient objects. The Bayes stereo matching algorithm and digital speckle method are introduced respectively. It verifies the feasibility of combining two methods. Meanwhile, it deduces theoretically the multi-probe system calibration, stitching the point cloud to a full shape. And the multi-probe system consists of multiple pairs of binocular. Experimental results show that the 3D reconstruction accuracy of the Bayes stereo matching algorithm is up to 0.08 mm at 500 mm distance between the object and the multi-probe system. It means that the point cloud number and point cloud accuracy improve significantly without loss of reconstruction efficiency.
出处
《计算机工程》
CAS
CSCD
北大核心
2017年第12期211-215,共5页
Computer Engineering
基金
西南科技大学校创新团队建设基金"高危作业环境的机器人场景智能感知系统"(14TDTK01)
关键词
Bayes立体匹配
数字散斑
多测量头标定
三维重建
Bayes stereo matching
digital speckle
multi-probe head calibration
3D reconstruction
