Abstract
The normal vector estimation of the large-scale scattered point cloud (LSSPC) plays an important role in point-based shape editing. However, the normal vector estimation for LSSPC cannot meet the great challenge of the sharp increase of the point cloud that is mainly attributed to its low computational efficiency. In this paper, a novel, fast method-based on bi-linear interpolation is reported on the normal vector estimation for LSSPC. We divide the point sets into many small cubes to speed up the local point search and construct interpolation nodes on the isosurface expressed by the point cloud. On the premise of calculating the normal vectors of these interpolated nodes, a normal vector bi-linear interpolation of the points in the cube is realized. The proposed approach has the merits of accurate, simple, and high efficiency, because the algorithm only needs to search neighbor and calculates normal vectors for interpolation nodes that are usually far less than the point cloud. The experimental results of several real and simulated point sets show that our method is over three times faster than the Elliptic Gabriel Graph-based method, and the average deviation is less than 0.01 mm.
Highlights
With the popularity of 3D scanning measurement technology, the acquisition of large-scale scattered point cloud (LSSPC) is easier due to the high resolution of the scanning devices
A new bi-linear interpolation based method for estimating the normal vector for LSSPC has been presented in this paper
The point cloud is segmented by many small cubes according to the been presented in this paper
Summary
With the popularity of 3D scanning measurement technology, the acquisition of large-scale scattered point cloud (LSSPC) is easier due to the high resolution of the scanning devices. The calculation of normal vectors based on ENN usually performs the following steps: First, sort the scattered point cloud into a classic data structure, such as a k-d tree [8] or hash table; search the nearest neighbor within a fixed quantity or a fixed distance of each point; at last, compute the normal vector using neighbor points for each point. These ENN-based methods are widely used, there are some difficulties to overcome [9,10].
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