A novel point cloud registration using 2D image features
A novel point cloud registration using 2D image features
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DOI：
10.1186/s13634-016-0435-y
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发表时间：
2017-01-07
影响因子：
1.9
通讯作者：
Chen, Yong-Sheng
中科院分区：
文献类型：
--
作者：
Lin, Chien-Chou;Tai, Yen-Chou;Chen, Yong-Sheng
由于三维扫描仪一次只能捕获三维物体的一个场景，因此多场景的三维配准是三维建模的关键问题。提出了一种基于二维局部特征匹配的三维配准方法。该方法首先将点云数据转换为二维方位角图像，然后利用基于二维特征的匹配方法SURF寻找两幅图像之间的匹配像素对。通过这些像素对可以得到三维点云的对应点。由于对应对是按匹配特征之间的距离排序的，因此仅使用对应对的上半部分来通过最小二乘近似找到最佳旋转矩阵。在本文中，最佳旋转矩阵的正交Procrustes方法（基于SVD的方法）。因此，可以通过将这些点云与最佳变换矩阵对准来重建物体的3D模型。实验结果表明，该方法的精度接近ICP，但计算量明显减少。其性能比广义ICP算法快六倍。此外，虽然ICP要求两个场景的高度对准相似性，所提出的方法是鲁棒的视角差异较大。
Since a 3D scanner only captures a scene of a 3D object at a time, a 3D registration for multi-scene is the key issue of 3D modeling. This paper presents a novel and an efficient 3D registration method based on 2D local feature matching. The proposed method transforms the point clouds into 2D bearing angle images and then uses the 2D feature based matching method, SURF, to find matching pixel pairs between two images. The corresponding points of 3D point clouds can be obtained by those pixel pairs. Since the corresponding pairs are sorted by their distance between matching features, only the top half of the corresponding pairs are used to find the optimal rotation matrix by the least squares approximation. In this paper, the optimal rotation matrix is derived by orthogonal Procrustes method (SVD-based approach). Therefore, the 3D model of an object can be reconstructed by aligning those point clouds with the optimal transformation matrix. Experimental results show that the accuracy of the proposed method is close to the ICP, but the computation cost is reduced significantly. The performance is six times faster than the generalized-ICP algorithm. Furthermore, while the ICP requires high alignment similarity of two scenes, the proposed method is robust to a larger difference of viewing angle.