A novel coarse-to-fine registration approach for aligning partially overlapped 3D scanned data

Tuladhar, Upendra; Lee, Gyu M.; Ahn, Seokyoung

doi:10.1080/0951192x.2017.1407450

Cited by 1 publication

(2 citation statements)

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“…At present, there are many research methods for point cloud matching. [2][3][4][5][6][7][8][9] The iterative closest point (ICP) algorithm 3 is one of the most popular methods. The optimal transformation matrix of a two-view point cloud is acquired through continuously reducing the Euclidean distance between the point clouds until the best match between the two views is achieved.…”

Section: Introductionmentioning

confidence: 99%

“…A third study 7 derived a matching model using the overlapping relationship of different planes in the point cloud and then designed a point cloud registration model based on point and plane features. Tuladhar et al 8 developed a two-step registration approach for aligning partially overlapped 3D scanned data, and the fine registration is the optimized step for the coarse step. The method improved the registration efficiency and accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Flexible point cloud matching method based on three-dimensional image feature points

Cui

Liao

Cheng

et al. 2018

Advances in Mechanical Engineering

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Flexible and robust point cloud matching is important for three-dimensional surface measurement. This article proposes a new matching method based on three-dimensional image feature points. First, an intrinsic shape signature algorithm is used to detect the key shape feature points, using a weighted three-dimensional occupational histogram of the data points within the angular space, which is a view-independent representation of the three-dimensional shape. Then, the point feature histogram is used to represent the underlying surface model properties at a point whose computation is based on the combination of certain geometrical relations between the point's nearest k-neighbors. The two-view point clouds are robustly matched using the proposed double neighborhood constraint of minimizing the sum of the Euclidean distances between the local neighbors of the point and feature point. The proposed optimization method is immune to noise, reduces the search range for matching points, and improves the correct feature point matching rate for a weak surface texture. The matching accuracy and stability of the proposed method are verified using experiments. This method can be used for a flat surface with weak features and in other applications. The method has a larger application range than the traditional methods.

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