An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds
The progressive TIN (triangular irregular network) densification (PTD) filter algorithm is widely used for filtering point clouds. In the PTD algorithm, the iterative densification parameters become smaller over the entire process of filtering. This leads to the performance—especially the...
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doaj-aed3979a4a024429ba3c9303e6f07d022020-11-25T00:46:48ZengMDPI AGISPRS International Journal of Geo-Information2220-99642018-10-0171040910.3390/ijgi7100409ijgi7100409An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point CloudsYouqiang Dong0Ximin Cui1Li Zhang2Haibin Ai3College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Xueyuan Road DING No. 11, Beijing 100083, ChinaCollege of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Xueyuan Road DING No. 11, Beijing 100083, ChinaChinese Academy of Surveying and Mapping, Lianhuachixi Road No. 28, Beijing 100830, ChinaChinese Academy of Surveying and Mapping, Lianhuachixi Road No. 28, Beijing 100830, ChinaThe progressive TIN (triangular irregular network) densification (PTD) filter algorithm is widely used for filtering point clouds. In the PTD algorithm, the iterative densification parameters become smaller over the entire process of filtering. This leads to the performance—especially the type I errors of the PTD algorithm—being poor for point clouds with high density and standard variance. Hence, an improved PTD filtering algorithm for point clouds with high density and variance is proposed in this paper. This improved PTD method divides the iterative densification process into two stages. In the first stage, the iterative densification process of the PTD algorithm is used, and the two densification parameters become smaller. When the density of points belonging to the TIN is higher than a certain value (in this paper, we define this density as the standard variance intervention density), the iterative densification process moves into the second stage. In the second stage, a new iterative densification strategy based on multi-scales is proposed, and the angle threshold becomes larger. The experimental results show that the improved PTD algorithm can effectively reduce the type I errors and total errors of the DIM point clouds by 7.53% and 4.09%, respectively, compared with the PTD algorithm. Although the type II errors increase slightly in our improved method, the wrongly added objective points have little effect on the accuracy of the generated DSM. In short, our improved PTD method perfects the classical PTD method and offers a better solution for filtering point clouds with high density and standard variance.http://www.mdpi.com/2220-9964/7/10/409the point clouds filteringthe PTD algorithmthe density and standard variance of point cloudsthe densification parameters |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Youqiang Dong Ximin Cui Li Zhang Haibin Ai |
spellingShingle |
Youqiang Dong Ximin Cui Li Zhang Haibin Ai An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds ISPRS International Journal of Geo-Information the point clouds filtering the PTD algorithm the density and standard variance of point clouds the densification parameters |
author_facet |
Youqiang Dong Ximin Cui Li Zhang Haibin Ai |
author_sort |
Youqiang Dong |
title |
An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds |
title_short |
An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds |
title_full |
An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds |
title_fullStr |
An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds |
title_full_unstemmed |
An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds |
title_sort |
improved progressive tin densification filtering method considering the density and standard variance of point clouds |
publisher |
MDPI AG |
series |
ISPRS International Journal of Geo-Information |
issn |
2220-9964 |
publishDate |
2018-10-01 |
description |
The progressive TIN (triangular irregular network) densification (PTD) filter algorithm is widely used for filtering point clouds. In the PTD algorithm, the iterative densification parameters become smaller over the entire process of filtering. This leads to the performance—especially the type I errors of the PTD algorithm—being poor for point clouds with high density and standard variance. Hence, an improved PTD filtering algorithm for point clouds with high density and variance is proposed in this paper. This improved PTD method divides the iterative densification process into two stages. In the first stage, the iterative densification process of the PTD algorithm is used, and the two densification parameters become smaller. When the density of points belonging to the TIN is higher than a certain value (in this paper, we define this density as the standard variance intervention density), the iterative densification process moves into the second stage. In the second stage, a new iterative densification strategy based on multi-scales is proposed, and the angle threshold becomes larger. The experimental results show that the improved PTD algorithm can effectively reduce the type I errors and total errors of the DIM point clouds by 7.53% and 4.09%, respectively, compared with the PTD algorithm. Although the type II errors increase slightly in our improved method, the wrongly added objective points have little effect on the accuracy of the generated DSM. In short, our improved PTD method perfects the classical PTD method and offers a better solution for filtering point clouds with high density and standard variance. |
topic |
the point clouds filtering the PTD algorithm the density and standard variance of point clouds the densification parameters |
url |
http://www.mdpi.com/2220-9964/7/10/409 |
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