Hole-Filling Algorithm for Airborne Lidar Point Cloud Data

Liang, Peng; Zhou, Guoqing; Lu, Yanling; Zhou, Xiang; Song, Bo

doi:10.5194/isprs-archives-xlii-3-w10-739-2020

Cited by 2 publications

(6 citation statements)

References 7 publications

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“…As expected, the filled results are the same as the original building. To better analyze the performance of the proposed method, it was compared with the MLS [8]. Figure 11 presents the filled results of some holes using the MLS and our method, respectively.…”

Section: Experimental Evaluation and Discussionmentioning

confidence: 99%

“…In contrast, the filled result by the proposed method can be smoothly connected with the original point cloud, which is similar to the distribution of the original building point cloud. Especially for the boundary, no discontinuity was To better analyze the performance of the proposed method, it was compared the MLS [8]. Figure 11 presents the filled results of some holes using the MLS an method, respectively.…”

Section: Experimental Evaluation and Discussionmentioning

confidence: 99%

“…Furthermore, although the artificial holes span two floors, the filled results using the proposed method are ideal. The filled results were further evaluated and analyzed using root mean square error (RMSE) [8]. Table 2 presents the RMSE of different filled results.…”

Section: Experimental Evaluation and Discussionmentioning

confidence: 99%

“…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%

“…The coordinate of the neighboring point q i is (x q , y q , z q ), and the coordinate of its projection point is q i x q , y q , z q . Thus, the projection point q i can be computed from Equation (8):…”

Section: Hole Boundary Extractionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Experimental Evaluation and Discussionmentioning

confidence: 99%

Section: Experimental Evaluation and Discussionmentioning

confidence: 99%

Section: Experimental Evaluation and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Hole Boundary Extractionmentioning

confidence: 99%

See 3 more Smart Citations

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

Min

Wang

et al. 2022

Remote Sensing

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A Novel Algorithm Based on Geometric Characteristics for Tree Branch Skeleton Extraction from LiDAR Point Cloud

Yang

Wen

Wang

et al. 2022

Forests

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More accurate tree models, such as branch skeleton, are needed to acquire forest inventory data. Currently available algorithms for constructing a branch skeleton from a LiDAR point cloud have low accuracy with problems such as irrational connection near trunk bifurcation, excessive central deviation and topological errors. Using the C++ and PCL library, a novel algorithm of the incomplete simulation of tree transmitting water and nutrients (ISTTWN), based on geometric characteristics for tree branch skeleton extraction, was developed in this research. The algorithm is an incomplete simulation of tree transmitting water and nutrients. Improvements were made to improve the time and memory consumption. The result show that the ISTTWN algorithm without any improvements is quite time consuming but has consecutive output. After improvement with iteration, the process is faster and has more detailed output. Breakpoint connection is added to recover continuity. The ISTTWN algorithm with improvements can produce a more accurate skeleton and cost less time than a previous algorithm. The superiority and effectiveness of the method are demonstrated, which provides a reference for the subsequent study of tree modeling and a prospect of application in other fields, such as virtual reality, computer games and movie scenes.

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Hole-Filling Algorithm for Airborne Lidar Point Cloud Data

Cited by 2 publications

References 7 publications

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

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

A Novel Algorithm Based on Geometric Characteristics for Tree Branch Skeleton Extraction from LiDAR Point Cloud

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