Yang Cui’s research while affiliated with Shenzhen University and other places

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Publications (3)


Automatic 3-D Reconstruction of Indoor Environment With Mobile Laser Scanning Point Clouds
  • Article

June 2019

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552 Reads

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78 Citations

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Yang Cui

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Three-dimensional (3-D) modeling of indoor environment plays an important role in various applications such as indoor navigation, Building Information Modeling (BIM), interactive visualization, etc. While automated reconstruction of 3-D models from point clouds is receiving more and more attention. Indoor modeling remains a challenging task in terms of dealing with the complexity of indoor environment, the level of automation and restrictions of input data. To address these issues, an automatic indoor reconstruction method that quickly and effectively reconstructs indoor environment of multi-floors and multi-rooms using both point clouds and trajectories from mobile laser scanning (MLS) is proposed. The proposed automatic method of parametric structure modeling comprises three steps. First, structural elements, such as doors, windows, walls, floors, and ceilings, are extracted based on the geometric and semantic features of point clouds. Then, the point clouds are automatically segmented into adjoining rooms through a combination of visibility analysis and physical constraints of the structural elements, which ensures the integrity of the room-space partitions and yields priors for the definition of point cloud label for reconstructed model. Finally, 3-D models of individual rooms are constructed by solving an energy optimization function via multi-label graph cuts. Three benchmark datasets collected by two handheld laser scanning (HLS) and a backpack laser scanning (BLS) system were used to evaluate the proposed method. Experiments demonstrate that the recall and precision of reconstructed surface models obtained by the proposed method are mostly larger than 60%, and the average F1-score of the model is close to 5 cm.


Table 2 . Main technical characteristics of Aquarius in shallow water mode.
Table 5 . The PSNR distribution of the averaged RMSE values for the three algorithms.
Table 6 .
Figure 7. Six randomly selected waveforms detected solely by the QF and IQF algorithms.| (a,c,e,g,i,k): QF algorithm; (b,d,f,h,j,l): IQF algorithm. 
Figure 8. The RMSE distribution of the detected waveform numbers for the three algorithms. Figure 8. The RMSE distribution of the detected waveform numbers for the three algorithms. 

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An Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms
  • Article
  • Full-text available

February 2018

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392 Reads

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20 Citations

Sensors

In this paper, an improved method based on a mixture of Gaussian and quadrilateral functions is presented to process airborne bathymetric LiDAR waveforms. In the presented method, the LiDAR waveform is fitted to a combination of three functions: one Gaussian function for the water surface contribution, another Gaussian function for the water bottom contribution, and a new quadrilateral function to fit the water column contribution. The proposed method was tested on a simulated dataset and a real dataset, with the focus being mainly on the performance of retrieving bottom response and water depths. We also investigated the influence of the parameter settings on the accuracy of the bathymetry estimates. The results demonstrate that the improved quadrilateral fitting algorithm shows a superior performance in terms of low RMSE and a high detection rate in the water depth and magnitude retrieval. What’s more, compared with the use of a triangular function or the existing quadrilateral function to fit the water column contribution, the presented method retrieved the least noise and the least number of unidentified waveforms, showed the best performance in fitting the return waveforms, and had consistent fitting goodness for all different water depths.

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A Triangular Prism Spatial Interpolation Method for Mapping Geological Property Fields

August 2017

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214 Reads

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11 Citations

ISPRS International Journal of Geo-Information

The spatial interpolation of property fields in 3D, such as the temperature, salinity, and organic content of ocean water, is an active area of research in the applied geosciences. Conventional interpolation methods have not adequately addressed anisotropy in these data. Thus, in our research we considered two interpolation methods based on a triangular prism volume element, as a triangular prism structure best represents directivity, to express the anisotropy inherent in geological property fields. A linear triangular prism interpolation is proposed for layered stratum that achieves a complete C0 continuity based on the volume coordinates of the triangular prism. A triangular prism quadric interpolation (a unit function of a triangular prism spline with 15 nodes) is designed for a smooth transition between adjacent triangular prisms with approximately C1 continuity, expressing the continuity of the entire model. We designed a specific model which accounts for the different spatial correlations in three dimensions. We evaluated the accuracy of our proposed linear and triangular prism quadric interpolation methods with traditional inverse distance weighting (IDW) and kriging interpolation approaches in comparative experiments. The results show that, in 3D geological modeling, the linear and quadric triangular prism interpolations more accurately represent the changes in the property values of the layered strata than the IDW and kriging interpolation methods. Furthermore, the triangular prism quadric interpolation algorithm with 15 nodes outperforms the other methods. This study of triangular prism interpolation algorithms has implications for the expression of data fields with 3D properties. Moreover, our novel approach will contribute to spatial attribute prediction and representation and is applicable to all 3D geographic information; for example, in studies of atmospheric circulation, ocean circulation, water temperature, salinity, and three-dimensional pollutant diffusion.

Citations (3)


... Conventional explicit data-driven surface modeling methods struggle to reconstruct complete shapes when encountering regions with large missing data. Therefore, many methods are developed using grammatical rules or pattern matching [4][5][6], and object-level modeling results are achieved by leveraging prior knowledge of indoor environments. The latest advances in 3D deep learning have facilitated the understanding of 3D indoor environments and automatic 3D modeling [1,7]. ...

Reference:

Building Lightweight 3D Indoor Models from Point Clouds with Enhanced Scene Understanding
Automatic 3-D Reconstruction of Indoor Environment With Mobile Laser Scanning Point Clouds
  • Citing Article
  • June 2019

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

... The CGD technique exhibits stable decomposition performance when applied to topographic LiDAR data in urban or forest areas where multiple return pulses are separate and have prominent peaks [23,24]. However, it may not be suitable for asymmetric ABL waveforms with a peak left shift due to attenuation of the return pulse energy [25,26], which can lead to point omissions for seafloor objects [18]. ...

An Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms

Sensors

... Earlier studies used gridded meteorological data interpolated from site measurements using mathematical interpolation [24][25][26][27] to provide input for the SEBS model. However, using mathematical interpolation from station measurements cannot effectively consider the heterogeneity of the underlying surface [28]. Most weather stations are scattered, and meteorological elements such as wind speed, humidity and other sensitive variables which are closely related to ET estimation are significantly discrepant due to the influence of the topography and surrounding environment [29,30]. ...

A Triangular Prism Spatial Interpolation Method for Mapping Geological Property Fields

ISPRS International Journal of Geo-Information