An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds

Dong, Youqiang; Cui, Ximin; Zhang, Li; Ai, Haibin

doi:10.3390/ijgi7100409

Cited by 14 publications

(7 citation statements)

References 29 publications

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“…Variance filtering method (Dong et al, 2018): the contribution of features to the prediction value lies in the difference between the feature values under the same feature, and if all of the feature values for a certain feature are the same, the feature has no influence on the outcome of the prediction. To get rid of comparable features, utilize the variance filtering method.…”

Section: Feature Selectionmentioning

confidence: 99%

Machine learning predictive model for electronic slurries for smart grids

et al. 2023

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Electricity is a fundamental energy that is essential to the growth of industrialization and human livelihood. Electric power resources can be used to meet living and production needs more steadily, effectively, and intelligently with the help of an intelligent power grid. The accuracy and stability of component requirements have increased due to the rapid growth of intelligent power networks. One of the fundamental components for component production is electronic slurry, so optimizing electronic paste’s properties is crucial for smart grids. In the field of materials science, the process of discovering new materials is drawn out and chance-based. The traditional computation process takes a very long time. Scientists have recently applied machine learning techniques to anticipate material properties and hasten the creation of novel materials. These techniques have proven to offer amazing benefits in a variety of fields. Machine learning techniques, such as the cross-validated nuclear ridge regression algorithm to predict double perovskite structure materials and the machine learning algorithm to predict the band gap value of chalcopyrite structure materials, have demonstrated excellent performance in predicting the band gap value of some specific material structures. The performance value of other structural materials cannot be predicted directly by this targeted prediction model; it can only forecast the band gap value of a single structural material. This study presents two model techniques for dividing data sets into element kinds using regression models and dividing data sets into clusters using regression models, both of which are based on the fundamental theory of physical properties, band gap theory. This plan is more efficient than the classification-regression model. The MAE dropped by 0.0455, the MSE dropped by 0.0425, and the R2 rose by 0.022. The effectiveness of machine learning in forecasting the material band gap value has increased, and the model trained by this design strategy to predict the material band gap value is more reliable than previously.

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Section: Feature Selectionmentioning

confidence: 99%

Machine learning predictive model for electronic slurries for smart grids

et al. 2023

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“…Many point-cloud filtering algorithms have been used for identifying ground points and nonground points (such as vegetation). These point-cloud filtering algorithms can be mainly classified as the morphological method [7,8], triangulated irregular network-based (TIN-based) algorithms [9], machine-learning-based algorithms [10], and mathematical morphology-based algorithms [11,12].…”

Section: Introductionmentioning

confidence: 99%

UAV-Based Terrain Modeling in Low-Vegetation Areas: A Framework Based on Multiscale Elevation Variation Coefficients

et al. 2023

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The removal of low vegetation is still challenging in UAV photogrammetry. According to the different topographic features expressed by point-cloud data at different scales, a vegetation-filtering method based on multiscale elevation-variation coefficients is proposed for terrain modeling. First, virtual grids are constructed at different scales, and the average elevation values of the corresponding point clouds are obtained. Second, the amount of elevation change at any two scales in each virtual grid is calculated to obtain the difference in surface characteristics (degree of elevation change) at the corresponding two scales. Third, the elevation variation coefficient of the virtual grid that corresponds to the largest elevation variation degree is calculated, and threshold segmentation is performed based on the relation that the elevation variation coefficients of vegetated regions are much larger than those of terrain regions. Finally, the optimal calculation neighborhood radius of the elevation variation coefficients is analyzed, and the optimal segmentation threshold is discussed. The experimental results show that the multiscale coefficients of elevation variation method can accurately remove vegetation points and reserve ground points in low- and densely vegetated areas. The type I error, type II error, and total error in the study areas range from 1.93 to 9.20%, 5.83 to 5.84%, and 2.28 to 7.68%, respectively. The total error of the proposed method is 2.43–2.54% lower than that of the CSF, TIN, and PMF algorithms in the study areas. This study provides a foundation for the rapid establishment of high-precision DEMs based on UAV photogrammetry.

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“…However, this research ignores filtering accuracy. Other incremental improvements to the PTD were also made in [28], [29].…”

Section: Introductionmentioning

confidence: 99%

A Fast Progressive TIN Densification Filtering Algorithm for Airborne LiDAR Data Using Adjacent Surface Information

Li¹,

Ye²,

Guo³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Point cloud filtering is a preliminary and essential step in various applications of airborne LiDAR (light detection and ranging) data, with progressive triangulated irregular network (TIN) densification (PTD) being one of the classic methods for filtering LiDAR point clouds. The PTD algorithm densifies ground points through iteration operation based on initial ground seed points. However, the poor performance in steeply sloped areas and time-consuming processing are serious drawbacks for PTD algorithms. In this paper, we propose a fast progressive TIN densification (FPTD) filtering algorithm for airborne LiDAR data using adjacent surface information. After carefully establishing parameters and removing outliers, our improved FPTD uses a sliding window to obtain significantly more initial ground seed points. And we modified some iterative determination criterion , including the definition of maximum relative elevation threshold and the introduction of signed computation, to eliminate avoidable non-ground points. Then adjacent surface information was utilized to iterate each point cloud block, which is the smallest unit that point cloud can be segmented. Additionally, the algorithm is easily run in a multi-threaded environment, further accelerating the filtering process to some extent. Experiments show that our proposed FPTD filtering algorithm is fast and robust. Compared to the PTD, the FPTD algorithm yields better error rates and kappa coefficients in 1/12 of the time required by the PTD.

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An Improved Progressive TIN Densification Filtering Method Considering the Density and Standard Variance of Point Clouds

Cited by 14 publications

References 29 publications

Machine learning predictive model for electronic slurries for smart grids

Machine learning predictive model for electronic slurries for smart grids

UAV-Based Terrain Modeling in Low-Vegetation Areas: A Framework Based on Multiscale Elevation Variation Coefficients

A Fast Progressive TIN Densification Filtering Algorithm for Airborne LiDAR Data Using Adjacent Surface Information

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