Abstract
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.
Highlights
During the last few decades, 3D scanner technology has rapidly developed and many different approaches have been proposed to build 3D-scanning devices that collect the shape data of objects and possibly their colors
This paper presents a novel 3D alignment method based on 2D local feature matching
4 Conclusions This paper presents a novel 3D alignment method based on 2D local feature matching
Summary
During the last few decades, 3D scanner technology has rapidly developed and many different approaches have been proposed to build 3D-scanning devices that collect the shape data of objects and possibly their colors. While 3D feature based approaches take more time in finding the corresponding point pairs between the reference and the source, registrations with 2D features can reduce the computation complexity by using 2D image matching. In [25], a novel approach for transforming a 3D point cloud to a 2D image was proposed, namely the bearing angle image.
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