Abstract
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.
Highlights
Three-dimensional (3D) point cloud matching is a key task in inverse engineering.[1]
This study developed a new robust matching method based on double neighborhood constraint optimization
The double neighborhood constraint steps consist of the following: If points P and Q are key feature points detected from cloud points P1 and P2, respectively, we can compute every key feature point’s fast point feature histogram (FPFH) within its local neighborhood: 1. Three points are sampled randomly from point set P to form set sp
Summary
Three-dimensional (3D) point cloud matching is a key task in inverse engineering.[1]. A highly discriminative shape feature is computed as a weighted 3D occupational histogram of data points in its spherical neighborhood using a 3D partition that evenly divides the angular space at a 3D point This choice of feature extraction ensures robustness in relation to the data noise, as well as any errors in computed reference frames. The double neighborhood constraint steps consist of the following: If points P and Q are key feature points detected from cloud points P1 and P2, respectively, we can compute every key feature point’s FPFH within its local neighborhood: 1. 3. For each pair of matched key feature points, the FPFH of neighborhood C2 is computed, and the Euclidean distance d2 is obtained. (b) Emin\Ecov, where Ecov is the minimum permissible error estimated based on the model
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