Reverse Engineering Point Clouds to Fit Tensor Product B-Spline Surfaces by Blending Local Fits

Tekumalla, Lavanya Sita; Cohen, Elaine

doi:10.48550/arxiv.1411.5993

Cited by 1 publication

(1 citation statement)

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“…Based on the high similarity between the hole and its neighborhood data, the existing methods perform interpolation repair on the hole by analyzing the data's spatial correlations and using them to fit the surface model where the hole is located [7]. The commonly used surface fitting algorithms include the moving least squares (MLS) method [8], B-spline curve method [9][10][11], radial basis function interpolation (RBF) [5], and deep learning method [12].…”

Section: Related Workmentioning

confidence: 99%

Hole Filling of Single Building Point Cloud Considering Local Similarity among Floors

Min

Wang

et al. 2022

Remote Sensing

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In the process of acquiring a point cloud, due to the shielding of the building itself and other ground objects (such as vegetation), the collected point cloud cannot cover the building facades surface uniformly and completely, and can produce several holes of different sizes. The existing hole-filling methods cannot obtain satisfying repaired results. To solve this problem, this paper proposes a new hole-filling approach considering the local similarity among floors. The proposed approach first detects holes in the building facade surface and then identifies the building floors; next, according to the fact that building data are similar in the same position on different floors, the holes of the building facade point cloud can be filled using the data of different floors. The study examines three large buildings to verify the proposed method. The experimental results show that the proposed method outperforms the state-of-the-art filling methods; the filling results of the proposed method are closer to the real shape and can be smoothly connected with the original building point cloud. Moreover, the proposed method has greater accuracy when comparing the root mean square error (RMSE).

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