
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12560-1
10.1016/j.heliyon.2024.e36529
e36529
Research Article
3D modelling method and application to a digital campus by fusing point cloud data and image data
Yuanyuan F.E.N.G. 1298791880@qq.com
ab
Hao L.I. c
Chaokui L.I. b⁎
Jun C.H.E.N. d
a Hunan University of Science and Technology, School of Resource & Environment and Safety Engineering, Xiangtan, Hunan, 411201, China
b Hunan University of Science and Technology, National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Xiangtan, Hunan, 411201, China
c Xiangtan Jinhao Software Development Co. Ltd., Xiangtan, Hunan, 411100, China
d Hunan Xingtian Electronic Technology Co. Ltd., Changsha, Hunan, 410006, China
⁎ Corresponding author. Hunan University of Science and Technology, National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Xiangtan, Hunan, 411201, China
19 8 2024
15 9 2024
19 8 2024
10 17 e365294 3 2024
22 7 2024
18 8 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Objective

The use of single-source data for real-world 3D modelling currently faces problems such as deformation, pulling and fuzzy texture at the bottom of buildings in some feature models because of the lack of images. Moreover, LIDAR generates a huge amount of data, and the massive raw data processing and point cloud parsing puts high demands on the hardware arithmetic and algorithms. Aiming at the deficiencies and defects of the two data sources of inclined photogrammetry and airborne laser point cloud in the construction of high-quality and high-precision city-level 3D models.

Methods

this study uses a university library building as an example and proposes the main technical process and method of modelling after fusing the point cloud data acquired by inclined photogrammetry and 3D laser scanning technology. This is accomplished in the reconstruction stage of multi-source data fusion through data spatial alignment, coordinate system unification and data spatial integration. At the stage of multi-source data fusion and reconstruction, through data spatial alignment, coordinate system unification, point cloud coarse alignment and the iterative closest point (ICP) algorithm, a realistic 3D model of a building is constructed to verify the effectiveness of the modelling method.

Results

The method can effectively improve the accuracy of the real-life 3D model, repair the deficiencies in the model and optimise the details of the model. It can also significantly improve the fineness of the tilt photography model and perfectly present the geometric and texture information of the building, making it a superior method for fine 3D reconstruction.

Conclusion

This 3D reconstruction method of buildings, which integrates low-altitude inclined photogrammetry and airborne light detection and ranging (LiDAR), has high positional accuracy and can provide new methods and new ideas for the construction of digital campuses as well as for other engineering applications.

Keywords

Oblique photogrammetry
Airborne laser scanning
Digital campus
Iterative closest point (ICP) algorithm
Texture effects
Accuracy analysis
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pmc1 Introduction

In recent years, surveying and mapping science and technology have developed rapidly, and several three-dimensional (3D) reconstruction data methods have emerged [[1], [2], [3], [4], [5], [6]], including oblique photogrammetry, light detection and ranging (LiDAR) and airborne LiDAR [[7], [8], [9], [10], [11]]. The oblique photogrammetric modelling technique has a wide operating range, is low cost and has high efficiency, making it suitable for constructing large-scale 3D models [12]. However, this method has drawbacks such as due to the limitation of the shooting angle in the air, the serious blockage of the ground part, and the complexity of the building structure, the constructed 3D model has the problems of model distortion, deformation, and voids [13,[13], [13], [14], [15]]. LiDAR technology is widely used in the 3D reconstruction of buildings because of its advantages such as high efficiency, high accuracy and high laser penetration. However, it has several shortcomings in practical modelling applications, due to the limitation of the instrument itself and the limitation of the observation angle by the height of the building, it is difficult to scan the middle and upper parts of the building completely, which leads to the construction of 3D models easily missing the top information [16,17]. The data acquisition method of airborne LiDAR overcomes the limitation of collecting a small amount of data in a single pass, which can effectively solve the difficulties in the digitisation of 3D spatial information. However, expressing the complete and rich details of the target object is challenging [[18], [19], [20]]. To address these shortcomings and limitations of a single approach, the fusion of oblique photogrammetry and LiDAR monitoring has recently become a new technology developed in the field of unmanned aerial vehicle (UAV) mapping [[21], [22], [23], [24]].

Currently, for the 3D modelling of buildings, oblique photogrammetry contains roof information taken at a vertical angle [25]. However, the problem of the inclination angle results in insufficient façade information near the ground to meet the need for detailed and realistic scene models [26]. Under the premise of realising inclined photogrammetry, a recent research hotspot is to use the ability of airborne LiDAR to penetrate clouds and vegetation, accurately measure and reflect real terrain information, achieve the required point accuracy through data processing of the airborne LiDAR laser point cloud and produce surveying and mapping results in accordance with accuracy requirements [27]. In this study, because of the advantages and disadvantages of both technologies, a hexacopter UAV and a 3D laser scanner were selected as the modelling equipment to take advantage of the strengths and avoid the shortcomings of the fusion of photogrammetric dense point cloud and laser point cloud, to generate a refined 3D model of the building and to perform quality evaluation in terms of the modelling effect and accuracy, with a view to providing a theoretical basis for the construction of the digital campus [28].

2 Overview of the study area

The experimental object selected for this paper is a university library building in Xiangtan, Hunan Province, as shown in Fig. 1. Study data collected at 2pm on October 27, 2023, the whole building is about 0.033km2, surrounded by high-rise buildings, roads, vehicles, dormitory areas, grasslands, woodlands and other features, the south door of the library has a large terrain undulation, the daily flow of people is large, and the east side of the tree shade, the measurement of the situation is more complex, in line with the requirements of the scene of the three-dimensional modeling of complex buildings.Fig. 1 Experimental subjects.

Fig. 1

3 Data sources

3.1 UAV oblique image acquisition

3.1.1 Route planning

Based on the planned station arrangement, the Zhihang SF1650 six-rotor UAV developed by Southern Surveying and Mapping Company was used to carry the T53P five-lens oblique camera, and oblique photogrammetry was performed on the buildings by means of flight routes, and five S-shaped routes were arranged to ensure the comprehensive coverage of the surveying and mapping area (Fig. 2). A total of 950 multi-view images were obtained, including 190 orthophotos and 760 oblique images, and the POS information has been written into the corresponding images (Fig. 3).Fig. 2 Route planning and design.

Fig. 2

Fig. 3 Partial orthophoto.

Fig. 3

3.1.2 Control point data

Laying image control points is conducive to improving the surveying and mapping accuracy of oblique photography, and the selection of their location and measurement of their coordinates directly affect the mathematical accuracy of the subsequent 3D modelling. The image control points should be evenly distributed within the survey area to ensure that they have a wide field of view. Therefore, RTK was used to lay and collect image control points, and the CGCS2000 coordinate system, which includes one base station point and five image control points, was adopted [29]. The coordinate data are shown in Table 1.Table 1 Control point coordinate data (m).

Table 1Point name	3D coordinates	
X	Y	Z	
JZD	393378.054	3088117.696	44.595	
XK1	393384.522	3087889.432	40.809	
XK2	393350.210	3087821.362	39.652	
XK3	393345.045	3087693.238	39.214	
XK4	393441.066	3087697.337	39.488	
XK5	393438.502	3087815.173	39.637	
Note, JZD in the table represents the base station point, and XK1-XK5 represents the image control point.

3.2 3D laser scanner field data acquisition

On the basis of completing the oblique image acquisition of the study area, the SAL-1500 multi-platform 3D laser mobile measurement system was used to scan the building façades. During the data acquisition process, UAV oblique photography and 3D laser scanning should be performed in the same period so that the collected UAV images and laser point cloud data have the most similar illumination effects. The system parameters are shown in Table 2.Table 2 SAL-1500 multi-platform 3D laser mobile measurement system and scanner parameters.

Table 2System parameters	Scanner parameters	
System Accuracy	±5 cm(Elevation/Plane)	Principle of operation	pulsed	
Operating Temperature	－20°∼＋55°	Scanning range	1.5–1500m	
Protection Grade	IP64	Ranging accuracy	15 mm(Single)
/5 mm(Repeat)	
Weight	3.89 kg	Maximum Spot Frequency	200 × 104 points/second	
Data storage	Removable memory stick	Field of view	0°–360°	
Control Mode	Automatic control and monitoring of radar status via aircraft ground station	Angular resolution	0.001°	
, also support PC/Mobile APP remote control	Data Results	Highly accurate 3D point cloud models and panoramic images	

3.3 Technological route

In this study, a combination of oblique photography and airborne laser scanning was used to model the experimental objects in three dimensions [[30], [31], [32]]. First, the UAS and 3D laser scanner were used to collect low-altitude images and 3D laser scanning data of the building. The external data were processed separately to align the 3D laser point cloud with the photogrammetric point cloud. The photogrammetric image and the aligned 3D laser point cloud were then fused to reconstruct the two types of multi-source data information to realise the 3D modelling of the building based on the multi-source data. The technological route is shown in Fig. 4.Fig. 4 Technical route.

Fig. 4

4 Multi-source point cloud fusion technology

4.1 Oblique image modeling process based on context capture

This method uses ContextCapture Centre 3D modelling software to integrate the image data, POS data and image control point data acquired from aerial survey field work and to obtain dense point cloud data through data pre-processing, aerial triangulation and 3D point cloud production.(1) Data pre-processing. This includes error correction, image resampling and the establishment of image pyramids. In addition, it is necessary to even out the acquired image information so that the basic colour tone of the image remains consistent.

(2) Control point marking and aerial triangulation. Before performing aerial triangulation encryption calculation, the stabbing point of the image control points is determined first, so that the coordinate data of the image control points collected by the external work corresponds to the coordinates of the points on the aerial camera film of the UAV, thereby improving accuracy [33]. The specific interface is shown in Fig. 5(a). The results of ContextCapture-based aerial triangulation calculations are crucial for 3D modelling. The aerial triangulation solution can automatically estimate the attributes of each input image group and the attitude information of the image. The results are shown in Fig. 5(b).Fig.e 5 Context capture data processing results.

Fig.e 5

Aerial triangulation methods can be used to calculate the position and distance of a target object on a photograph. By measuring the position of the object on the photograph at different angles and then using the principle of triangulation, the distance of the object relative to the photographic point can be calculated. In addition it has certain requirements for the selection of the measuring point. The location of the measurement point should be able to cover all angles and sides of the object being measured. Secondly, errors during the measurement process can also have an impact on the results. Due to various factors, such as atmospheric conditions, instrument accuracy, and terrain relief, the measurement results are often not completely accurate.(3) Oblique image dense point cloud generation. After aerial triangulation, tile differentiation, range and geometric constraint determination and other operations are performed to generate a 3D point cloud model [34]. A large amount of redundant feature information is collected during UAV data acquisition, stored in aerial tiles and processed using ContextCapture to generate 3D models of irrelevant features. Because redundant data affect the accuracy of point cloud matching and increases the burden on computer data processing, when outputting the point cloud, the data frame includes only the target features as much as possible and removes redundant features such as vegetation around the target features to obtain the oblique photogrammetric building point cloud data and the 3D model, as shown in Fig. 6, Fig. 7.Fig. 6 Oblique image dense point cloud.

Fig. 6

Fig. 7 Realistic 3D model constructed based on oblique photogrammetry technology.

Fig. 7

4.2 Laser point cloud processing flow based on southLidar pro

The original point cloud data are acquired using a 3D laser scanner through a series of data processing and other processes. Finally, the resultant point cloud with coordinate information is obtained [12]. Because of the airborne laser equipment, the coordinates of each air strip are self-contained after the flight.

The general processing flow of SouthLidar Pro is as follows.(1) Start a new project.

(2) Import raw data, including multi-shelf data fast solving, one-key fusion and other operations.

(3) Pre-process data, mainly for point cloud denoising and point cloud filtering. In the point cloud processing process, filtering is the first step of pre-processing, which has a big impact on the follow-up, only in the filtering pre-processing will the noise points, outliers, holes, data compression, etc. be customised in accordance with the follow-up processing, so as to better carry out the alignment, feature extraction, surface reconstruction, visualisation and other subsequent application processing.

(4) Convert coordinates by associating selected control points with point cloud positions in the software.

(5) Extract features, specifically the point cloud of the target building, providing basic data for the next step of alignment and fusion with the dense point cloud of tilt photography, as shown in Fig. 8(a).Fig. 8 Laser point cloud processing results.

Fig. 8

(6) Export RGB point cloud data, as shown in Fig. 8(b).

4.3 Point cloud fusion technology modeling process

4.3.1 Principle of ICP algorithm

The coarse alignment of the point clouds provides a more accurate initial position for spatial alignment. To further improve the accuracy of the spatial alignment, iterative closest point (ICP) alignment should be performed for both point clouds [35]. The principle of this method is the nearest-point iteration algorithm [36], which is commonly used for point cloud alignment. It determines the nearest set of corresponding points, solves the transformation matrix parameters and then iterates until it reaches the set value of the number of iterations or stops when the optimal alignment convergence principle is reached.

Let the source point cloud be P and the target point cloud be X. The distance d between them is as follows:(1) d(P,X)=min(x∈X)x−P

Seven parameter vectors are used to represent the rotation and translation transformations of the point set, as shown in equation (2):(2) X=(q0,qx,qy,qz,tx,ty,tz)TX0=[1,0,0,0,0,0,0]ms=|±[△x*△x]*[△y*△y]n|MP=|±∑i=1n[(Xi−Xj)2−(Yi−Yj)2]n|MZ=|±∑i=1n[(Zi−Zj)2]n|

Where, with q02+qx2+qy2+qz2=1 as constraints, the initial value of the seven parameter vector is set to X0=[1,0,0,0,0,0,0,]。

4.3.2 Point cloud alignment and fusion modeling based on ICP algorithm

(1) Alignment of the tilt photogrammetry point cloud and the 3D laser scanning point cloud

The main steps are as follows.① Import dense point cloud and laser point cloud data.

② Perform coarse alignment based on points with the same names from the two types of point clouds such that the point clouds are in the same coordinate system and their positions are close to each other. The main purpose of coarse alignment is to provide better initial point cloud iteration positions for subsequent fine alignment. Without the better initial iteration positions provided by coarse alignment, the ICP algorithm often falls into the local optimal solution, but not into the global optimal solution [37,38]. The selection of common points in the coarse alignment process is crucial, and at least three pairs of common points are required. The common points are generally selected from the parts where the point cloud model is more effective and obvious, such as the corners of the room and the obvious areas of the building texture. After coarse alignment, the overlap between the two types of point clouds is not sufficient for point cloud fusion, and further precise alignment is required.

③ Fine alignment by ICP algorithm. It provides more accurate point cloud alignment results and further reduces the error. Through multiple iterations, the alignment accuracy can be further improved. A report on the accuracy of multi-source point cloud alignment is shown in Fig. 9. It meets the alignment accuracy requirements and can be used for point cloud fusion modelling.Fig. 9 Accuracy of the multi-source point cloud alignment.

Fig. 9

④ Point cloud data processing and editing, delete the data in the middle and lower parts of the building and the data under the eaves in the tilt photography point cloud and keep only the data on the top of the building.

⑤ Fuse the point cloud and re-model.

(2) Point cloud fusion modelling

The aligned 3D laser point cloud is imported into the ContextCapture Centre software, and the photogrammetric multi-view image and 3D laser point cloud data are used as the data source for joint airborne triangulation. This is followed by the reconstruction process, which involves constructing the triangular mesh, texture mapping and other related processes to realise the construction of the real-life 3D model of the building (Fig. 10).Fig. 10 Realistic 3D model of a campus library constructed based on multi-source data fusion.

Fig. 10

5 Modeling results and accuracy analysis

The accuracy evaluation of the 3D model constructed after fusion refinement mainly includes two aspects: texture accuracy evaluation and geometric accuracy evaluation [39]. The texture accuracy evaluation takes the clarity of the surface texture of the model as the judgement standard, and the geometric accuracy evaluation still adopts the medium error as the evaluation index.

5.1 Point cloud fusion model texture effect analysis

The 3D model with point cloud data can effectively compensate for the lack of geometric structure and texture in areas such as the bottom of the building (which is more heavily shaded), signage and the building façade to improve the finesse and completeness of the 3D model and achieve the expected research purpose (Fig. 11).Fig. 11 Comparison of the texture effects in the oblique photogrammetric and fusion models.

Fig. 11

Comparing the fused models with the oblique photogrammetric models, it can be seen that problems such as convex packet distortion and deformation have been significantly improved. As shown in Fig. 11, building fusion models (b, d and f) are compared with oblique photogrammetric building models (a, c and e): ① The geometric structure of the fusion modelling model is complete. There is no missing data on the top of the building, and the building adheres to the surrounding vegetation. Although the detailed structure under the eaves is lost, the building's structural features, such as the ‘Junding Graphic’ logo, are clear and realistic. ② The fusion model mapping exhibits no deformation and raffia phenomenon. ③ Data acquisition is completed in the same period, and the fusion model has uniform colour tone, with no difference between light and dark areas. In summary, the fusion of oblique photography and LiDAR point cloud can effectively improve the accuracy of the real-life 3D model, repair model loopholes and optimise model details.

5.2 Point cloud fusion model accuracy analysis

To verify the validity of the fusion method using UAV images and 3D laser scanning to construct a real-life 3D model, it is analysed and compared with the single UAV modelling method in terms of the error and point accuracy of their aerial triangulation results.

5.2.1 Error analysis of airborne triangulation results

According to the accuracy specifications aerial photography triangulation, theoretical accuracy consists of two parts: image element accuracy and GCP (global control point) accuracy [40].(1) Ensure image element accuracy. After calculation, the root mean square error of re-projection is 0.64 pixel after incorporating the GCP into the parity solving, which meets the requirement for image element accuracy.

(2) Improve GCP accuracy. This refers to the accuracy of the GCP after participating in the aerial triangulation levelling difference and is mainly analysed in terms of plane and vertical components [41]. The formula is as follows:

(3) ms=|±[△x*△x]*[△y*△y]n|

where ms is the control point plane accuracy (m), n is the number of control points, Δx is the X-direction error and Δy is the Y-direction error.

Fig. 12 presents a comparative analysis of the aerial triangulation results for each parameter of the photogrammetric and point cloud fusion models.Fig. 12 Comparison of the aerial triangulation results for each parameter of the photogrammetric and point cloud fusion models.

Fig. 12

After the statistical analysis of the accuracy report of the aerial triangulation results of the photogrammetric and point cloud fusion models, it was observed that the re-projection error, the middle error of the ray distance, the middle error of the plane and the middle error of the elevation in the aerial triangulation results of the point cloud fusion model are smaller than the values of the aerial triangulation in the photogrammetric model. These findings align with the requirements for the next step of fusion modelling.

5.2.2 Point accuracy analysis

The evaluation of absolute accuracy of the real-life 3D model includes both planar accuracy and elevation accuracy [6,[42], [43], [44]]. The results of five checkpoints collected by RTK are taken as the true value, and the coordinate results obtained on the model are statistically different from the true value [45]. The formula is as follows:(4) MP=|±∑i=1n[(Xi−Xj)2−(Yi−Yj)2]n|

(5) MZ=|±∑i=1n[(Zi−Zj)2]n|

Where, MP、 MZ denote the plane accuracy and elevation accuracy, respectively. The smaller their values, the higher the fusion accuracy. n is the number of checkpoints, and (Xi, Yi, Zi) and (Xj, Yj, Zj) are the measured values of the latitude and longitude coordinates, respectively. The calculation results of point accuracy are shown in Table 3.Table 3 Point accuracy statistics.

Table 3Modeling Methods	Planar Accuracy (MP/m)	Elevation accuracy (Mz/m)	
Photogrammetric Modeling	0.1371	0.0323	
Point cloud fusion model	0.0898	0.0119	

As shown in Table 3, the root mean square of the planar error of the fused model is 0.0898 m, and that of the elevation error is less than 0.040 m, thus meeting the general requirements of digital campus construction. Therefore, it can be concluded that the fusion of these two techniques can obtain a complete building model with relatively high accuracy.

6 Results

Using more complex buildings as an example, a 3D modelling method based on the fusion of UAV photogrammetry and airborne LiDAR was proposed. After field data acquisition, point cloud data processing and 3D modelling, the construction of a real-life 3D model of a building was realised, and the 3D modelling quality evaluation was performed. The conclusions are as follows.(1) From the perspective of model texture effects, the UAV data acquisition method combined with 3D laser scanning solves the problems of low efficiency of ground-level near-view data acquisition and insufficient density or missing texture of the target's top surface caused by single data acquisition methods. The method ensures more comprehensive data acquisition.

(2) The aerial triangulation results indicate that the re-projection error, ray distance error, plane error and elevation error in the point cloud fusion model are smaller than those in the photogrammetric model, which are in line with the accuracy requirements of the fusion modelling.

(3) In terms of accuracy comparison, the fusion modelling method using 3D laser scanning improves the absolute plane accuracy of the real 3D model by approximately 34.5 % and the absolute elevation accuracy by approximately 63.2 % compared with those generated using the single UAV modelling method.

7 Conclusion

In summary, the proposed 3D modelling method of high-rise buildings overcomes the limitations of single-technology modelling, effectively solves the problem of missing geometry and texture in high-rise building modelling and exhibits good performance in terms of model refinement, texture quality and geometric accuracy. Simultaneously, this fusion modelling method can be used in the fields of protection of important buildings in mining areas and monitoring of geological hazards in mines, providing strong technical support for the popularity of the construction of real 3D China.

The full-element topographic maps collected internally through the ‘point cloud + oblique model’ multi-source data fusion provide powerful data support for the standardisation, refinement and scientific management of the city's 3D model. Moreover, they effectively address the problems of low data collection efficiency, high cost and significant security risks in field collection under the traditional measurement mode. Simultaneously, the enhancement of the ecological, economic, cultural and social benefits of the city is realised.

In today's diverse architectural styles, the mapping data fusion processing scheme does solve the technical bottleneck of refined real-view modelling to a certain extent. However, within the fusion category, this study has not yet explored air–ground fusion, and the aerial triangulation and fusion stability are still important issues facing the current multi-source data fusion technology. This is the direction to be focused on in subsequent relevant tests, studies and summaries.

Data availability statement

Data openly available in a public repository. (The data that support the findings of this study are openly available in [repository name] at [URL].)

Funding

This article was jointly funded by the National Natural Science Foundation of China (No. 42171418 ); The project of Natural Resources 10.13039/501100019081 Science and Technology Program of Hunan Province (20230122CH ); The open project of Hunan Geospatial Information Engineering and Technology Research Center (HNGI2023005 ); Hunan High-level Talent Gathering Project (22022RC4039 ).

CRediT authorship contribution statement

F.E.N.G. Yuanyuan: Writing – review & editing, Writing – original draft, Methodology, Data curation. L.I. Hao: Software. L.I. Chaokui: Supervision, Project administration, Investigation. C.H.E.N. Jun: Resources.

Declaration of competing interest

1. We confirm that neither the manuscript nor any parts of its content are currently under consideration or published in another journal.

2. All authors have approved the manuscript and agree with its submission to Heliyon.
==== Refs
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