KR102670898B1 - Construction Management: Development of 3D engine-based geometric optimization method for Building Information Model Visualization in Augmented Reality - Google Patents

Abstract

The 3D engine-based geometric optimization method for visualizing building information models in augmented reality includes the following steps: checking the geometric mesh information of the original model loaded using 3D engine properties, checking vertices and triangles in the original model, and verifying vertices in the original model. Extracting and saving the triangle mesh, deleting the triangle mesh identified in the original model based on curvature calculation for boundary confirmation, setting the reduction value, and creating a new vertex by applying the triangle center formula based on barycentric coordinates It includes a step of reducing and a step of reducing vertices to provide an optimized model.

Description

3D engine-based geometric optimization method for building information model visualization in augmented reality {Construction Management: Development of 3D engine-based geometric optimization method for Building Information Model Visualization in Augmented Reality}

The present invention relates to a 3D engine-based geometric optimization method for visualizing building information models in augmented reality. More specifically, the model built using Revit is linked to the 3D engine and uses the principle of triangular midpoint calculation consisting of three points. Apply it to find the triangle vertices of the 3D model. Then, the BIM model is optimized by reconstructing each vertex and creating new mesh information. The optimized BIM model uses an AR engine to connect to a cloud server with an AR device and visualize the BIM model in augmented reality. It is about a 3D engine-based geometric optimization method for visualizing building information models in augmented reality.

As projects become increasingly complex, Building Information Modeling (BIM) technology has evolved to merge projects around a digital twin of the information needed for collaboration. BIM stands for Building Information Management, which allows you to perform tasks and processes to create objects and information, add and share information, and manage processes throughout design, construction, and maintenance. BIM is widely applied in the construction and civil engineering fields, and attempts are being made to apply it to all stages before and after construction. BIM, with its geometric and semantic data, allows users to virtually test alternative designs, predict problems that may arise during construction, and interact with each building component in three-dimensional (3D) space. The integration of BIM and augmented reality (AR) can also enhance visualization of different stages of the project life cycle and ultimately increase the applicability of BIM in field work. Numerous ways to integrate AR and BIM have been proposed by scholars. AR is a technology that provides users with a real virtual view by combining digital information and real information on a virtual screen such as a tablet or HoloLens. AR enables real-time visualization of BIM information in the actual physical environment, especially during the project, maintenance and operation phases of buildings.

The construction industry is building and utilizing BIM models to efficiently manage collected data. Most BIM models in the architecture, engineering, and construction (AEC) field have large storage sizes due to their irregular geometry, density of components, and information complexity. Construction site data is being digitized through BIM models, but BIM models have many difficulties expressing information in web-based platforms or AR device environments due to the vast amount of data. In addition, in order to develop into a future construction industry through construction automation, augmented reality-based construction information visualization that connects the real world and the virtual world is essential. Converting BIM to AR requires converting the model to neutral file formats such as IFC, FBX, and OBJ for importing into the 3D engine. In the process of converting large BIM models to these neutral file formats, the storage data size must be reduced to improve data loading speed, minimize vertigo during visualization, and realize the actual size of the model. Additionally, the sheer size and complexity of the model requires a mesh reconstruction algorithm or technique that can reduce geometric information, as it takes a long time to transfer and requires a lot of computational effort to render.

In the past few years, BIM has attracted more attention in the field of AEC because it promotes information exchange and interoperability in digital format and maintains data about building facilities, which can be used to support better decisions in all processes of project management. received. Overall Project AR has been widely applied in the AEC field to better visualize 3D models generated in BIM software. AR technology has been adopted in the AEC field for more than 10 years. AR merges digital data with the real world, enabling clearer visualization of projects, better planning and decision-making, better assembly of components, and improved facility monitoring. AR in architecture and engineering includes design review, structural analysis, and daylight analysis. At construction sites, AR can help with site inspections, construction simulation, and safety management. In other areas, such as operations and maintenance, AR has been implemented for emergency evacuation and facility maintenance.

The integration of BIM and AR has been studied to improve visualization of 3D models. It has been explored in various fields of the construction industry. Research areas include on-site construction process control, construction safety management and visualization, construction collaboration, and multi-user discussion. However, efficient model optimization methods that simplify geometric data to improve model transfer and operation capabilities are still lacking.

In particular, in the process of transferring data from BIM to AR, large and complex models require more storage space, model transfer time and data processing workload increase during rendering, and visualization efficiency may decrease when using AR devices.

Therefore, the purpose of the present invention is to optimize and visualize the BIM model by applying the triangle midpoint calculation principle to the 3D engine to reconstruct each vertex and create new mesh information to prevent distortion of the shape and size of each member of the original model. The goal is to provide a 3D engine-based geometric optimization method for visualizing building information models in augmented reality for construction management.

A 3D engine-based geometric optimization method for visualizing a building information model in augmented reality according to the present invention to achieve the above object includes the steps of checking geometric mesh information of an original model loaded using 3D engine properties; Confirming vertices and triangles in the original model; Extracting and storing vertices from the original model; Deleting the triangle mesh identified in the original model based on curvature calculation for boundary confirmation; Setting a reduction value; Reducing vertices by creating new vertices by applying a centroid formula based on barycentric coordinates; and providing an optimized model by reducing vertices. The efficiency of model optimization is improved by simplifying the geometric data by deleting the triangle mesh identified in the original model by excluding and reducing non-contractible edges in the original model, and reorganizing and optimizing large-capacity construction BIM models in mesh form and visualizing them. Construction managers can increase their work efficiency by visualizing the optimized model without any changes or errors.

Here, the step of extracting and storing vertices from the original model is absent because optimization of the target 3D model is performed while the shape of the structure is maintained when extracted from the mesh configuration information constituting the surface matching the geometric and topological characteristics. This is desirable because deformation, member invasion, and combination and deletion phenomena between members may not occur.

In addition, it is preferable to further include the step of separating shape information by excluding non-contractible edges before deleting the triangle mesh identified in the original model, as distortion of the shape and size of each member of the original model may not occur. .

Here, in the step of reducing the vertices, if the extracted point cloud set is performed in a 3D engine such as Unity using C#, the data can be efficiently compressed through BIM model optimization while maintaining a high level of agreement compared to the original model. It is desirable because it can be seen.

And after the step of reducing the vertices, when the set vertex value is reached, reconstructing the triangle mesh using the generated vertex information; It is desirable to further include the step of storing the reconstructed triangle mesh information and visualization information, as it allows commercial smart devices to be actively used for work management at construction sites, thereby improving the efficiency of construction site management work.

According to the present invention, the efficiency of model optimization is improved by simplifying the geometric data by deleting the triangle mesh identified in the original model by removing and reducing the non-shrinkable edges in the original model, and by reorganizing the large construction BIM model into a mesh form. It has the effect of increasing the work efficiency of construction managers by optimizing and visualizing the optimized model without visual changes or errors.

In addition, since the optimization of the target 3D model is carried out while the shape of the structure is maintained, member deformation, member invasion, and combination and deletion phenomena between members may not occur, and distortion of the shape and size of each member of the original model may occur. There are effects that may not work.

In addition, through BIM model optimization, data can be efficiently compressed while showing a high level of agreement compared to the original model, and commercial smart devices can be actively used for work management at construction sites, increasing the efficiency of construction site management work. There is an effect that can be improved.

Figure 1 is an example diagram of a conceptual framework of 3D engine-based geometry optimization for augmented reality visualization.
Figure 2 is an example diagram of a mesh optimization process.
Figure 3 is a flowchart of the mesh reconstruction algorithm according to the present invention.
Figure 4 is a 2D illustration of the algorithm steps.
Figure 5 is an illustration of the triangle center formula.
Figure 6 is an example diagram of the OOO Bridge and Engineering Building 2 modeling process.
Figure 7 is an example of the existing model, the optimized model of the OOO Bridge, and the internal glass structure (Mesh Form).
Figure 8 is an example of the existing model, the optimized model of the OOO Bridge, and the internal circular pillar structure (mesh form).
Figure 9 is an example diagram comparing before and after applying the algorithm to the OOO University steel bridge in the 3D engine.
Figure 10 is an example diagram comparing before and after applying the algorithm to the OOO University Engineering Building in the 3D engine.
Figure 11 is an example of a model visualization test optimized with an AR device in a laboratory.
Figure 12 is an exemplary conceptual framework of 3D engine-based geometric optimization for augmented reality (AR) visualization.
Figure 13 is an example of visualizing the optimized model on an AR device.
Figure 14 is a 3D illustration of the algorithm steps.
Figure 15 is a flowchart of a 3D engine-based geometric optimization method for visualizing building information models in augmented reality according to the present invention.

Hereinafter, a 3D engine-based geometric optimization method for visualizing a building information model in augmented reality according to a preferred embodiment of the present invention will be described in detail with reference to the attached drawings.

The 3D engine-based geometric optimization method for visualizing building information models in augmented reality according to the present invention includes the steps of checking geometric mesh information of an original model loaded using 3D engine properties, checking vertices and triangles in the original model, Extracting and saving vertices from the original model, deleting the triangle mesh identified in the original model based on curvature calculation for boundary confirmation, setting a reduction value, and applying a triangle center formula based on barycentric coordinates to create new vertices. It includes the steps of generating and reducing vertices and providing an optimized model with reduced vertices.

Figure 15 is a flowchart of a 3D engine-based geometric optimization method for visualizing building information models in augmented reality according to the present invention.

A 3D engine-based geometric optimization method for visualizing building information models in augmented reality according to an embodiment of the present invention is as follows.

Import the original BIM model using FBX, RVT, and OBJ (S1).

Check the geometric mesh information of the original model loaded using 3D engine properties (S2).

Check the vertices and triangles in the original model (S3).

Vertices are extracted and stored from the mesh composition information that constitutes the surface that matches the geometric and topological characteristics of the original model (S4).

Shape information is separated by excluding edges that cannot be contracted (S5).

Delete the triangle mesh identified in the original model based on curvature calculation for boundary confirmation (S6).

Set the reduction value (S7).

Create a new vertex by applying the triangle center formula based on barycentric coordinates (S8).

The extracted point cloud set is vertex reduced in a 3D engine such as Unity using C# (S9).

When the set vertex value is reached, the triangle mesh is reconstructed using the generated vertex information (S10).

Store the reconstructed triangle mesh information and visualization information (S11).

Vertices are reduced to provide an optimized model (S12).

In the present invention, a geometric optimization method for BIM appearance information was designed to effectively deliver large-capacity BIM models to construction managers. This method generates mesh information by applying the triangle weighting principle to the BIM model. According to the principle of the center of a triangle, the point where the three vertical lines of a triangle meet is called the center of the triangle, and the center can be defined as the intersection of the three lines. The median represents the line connecting the midpoint of one side of a triangle and the vertex of the opposite side, and the center can be calculated by taking the average of the coordinates of the vertices (x, y, z) of the triangle.

The BIM model is linked to the 3D engine, and a mesh reduction algorithm is then applied inside the game engine. Game engines provide technology that can be used to create three-dimensional (3D) and two-dimensional (2D) games, as well as interactive simulations and other experiences. You can build an augmented reality environment that can turn real space into a digital space. In order to build augmented reality in a game engine, a script must be attached to the target object, and the triangle center principle algorithm is written in the code of the script to reconstruct the model mesh. The generated 3D model is visualized on an AR device (tablet, HoloLens) based on the AR engine. The visualized 3D model can apply surface origin-based technology to the real world to implement an augmented reality (AR) environment and visualize the 3D model in the real world. If data can be efficiently compressed through lightweighting of the optimal BIM model while showing a high level of agreement compared to the original model, commercial smart devices can be actively used to manage construction site work, improving the efficiency of construction site management work. do. The conceptual framework for the present invention is shown in Figures 1 and 12.

(Experiment)

1. Model optimization method

Polygonal modeling is an approach to modeling objects by describing or approximating their surfaces using a polygonal mesh. Polygonal models are used extensively in many areas of computer graphics, such as movies, computer games, and advertising. The complexity of these models in terms of number of polygons poses great challenges to the computing performance of the hardware. Most models used in the AEC field are particularly large and complex. It is common for a single building model to have many components, and when multiple buildings are included, the number of components inevitably becomes larger, so the number of polygons can become massive. Processing this kind of model without optimization involves a huge computational workload on the hardware. Model optimization methods can provide developers with a solution to simplify the model by simplifying the number of polygons, which can reduce data processing work in hardware. The polygon optimization method provides a solution for developers struggling with complex models as shown in Figure 2. These techniques simplify the polygonal geometry of non-critical parts of the model without significant loss of visual content of the scene.

Various mechanisms have been developed for polygon reduction, and the four main polygon removal mechanisms are vertex merging, sampling, adaptive subdivision, and decimation. Hoppe introduced progressive mesh as the first dynamic optimization algorithm for general polygon reduction by defining three types of mesh transformations: edge reduction, splitting, and swap. The surface simplification algorithm developed by Garland and Heckbert can use a quadratic error metric to collapse arbitrary pairs of vertices that do not need to be connected by edges. The vertex clustering method was approached by placing a 3D grid over the model to reduce the number of polygons and reducing all vertices within each cell to the most important vertices within the cell. There are also other approaches for model optimization, such as sampling and adaptive segmentation.

However, most existing model optimization methods are complex and have limitations in properly processing the mesh while maintaining the basic shape of the 3D model. In this paper, we present a 3D engine-based mesh reconstruction method specifically designed for AR visualization. This method addresses the shortcomings of previous quadratic error metrics (QEM) approaches by leveraging the insight that the most appropriate boundary edges can be reduced while lying along the curvature. Additionally, our algorithm uses a barycentric coordinate-based triangle centroid method to reduce vertices and reconstruct the triangle mesh.

After this, we briefly describe the basics of the basic QEM algorithm for completeness. After that, we explain how to take the boundary mesh into account using curvature calculations.

1-1. Quadratic error metric method Garland and Heckbert developed the QEM algorithm and proposed a quadratic error method. This algorithm mainly focuses on the iterative reduction process of vertex pairs and the assignment of the error quadratic matrix. The error at a _vertex is defined ^as v ₌ [ _v To attempt to characterize the error at each vertex to define the shrinkage cost by choosing which shrinkage to perform during a given iteration, we associate a symmetric 4 × 4 matrix Q with each vertex and the folding cost of the model is computed quadratic. The form △(v)=v ^T Qv·T The plane is denoted by p=[abcd] ^T and is defined by the equation of the three-dimensional space ax+by+cz+d=0. Here a ² +b ² +c ² =1, d is a constant. This can be defined as the vertex error as the sum of the squares of the distance to the plane.

△(v)=△([vx, vy, vz 1]T)=∑/p∈pianes(v)(p ^T v) ²

Here, K _p , the set of relevant triangles, is the fundamental error quadratic of p (see Equation 1 below).

[Formula 1]

The secondary error matrix of vertex v is given in Equation 2 below.

[Formula 2]

Q1 + Q2 can be regarded as a quadratic error matrix of three vertices v', where (v^') is the error cost of the new vertex v, and the folding cost of the edge folding operation is given in Equation 3 below.

[Formula 3]

.

1-2. Curvature calculation based verification method for border mesh

To obtain better boundary constraints, it is important to consider the shape of the boundary curve. By removing the vertices of the linear boundary, we can maintain a better approximation of some of the vertices of the interior surface. The QEM algorithm developed by Garland and Heckbert was an approach to mark and preserve important boundary boundaries during the initialization process, but it is not without its own limitations. Their algorithm oversimplifies the mesh with boundary constraints because it removes most surface edges and vertices before collapsing the boundary vertices. Bahirat developed curvature-based boundary preservation inspired by the original QEM mesh simplification approach. They considered boundary vertices v1 and adjacent boundary vertices v2 and v3. This is because every boundary vertex has exactly two adjacent boundary vertices.

We used the approach to calculate the curvature of the boundary curve k at vertex v1. By adding the quadratic of the boundary constraint plane to both endpoints of the boundary edge, we can obtain a better approximation across the surface edge by removing some vertices of the linear boundary with Eq. Here Q(v1) and Q(v2) take the associated quadratic matrix vertices v1 and v2 respectively. Wb is a user-defined weight coefficient. k(v1) and k(v2) are the boundary curvatures at vertices v1 and v2, respectively. Qbcp is the quadratic of the boundary constraint plane at edges (v1, v2) (see Equation 4 below).

[Formula 4]

2. Geometric model optimization for BIM

Geometric optimization is an important step in the AEC model optimization process. It refers to deleting or modifying the model's mesh that has a relative impact on the overall shape, and aims to reduce the size of the model while maintaining the original shape. Importing models into a 3D engine requires a neutral file format (e.g. IFC, FBX, OBJ), and since most AEC models are created in BIM software, they can be exported to a neutral file format. BIM models in 3D engines (Unreal Engine, Unity, etc.) use BIM software's Boundary Representation (B-Rep) and Constructive Solid Geometry (CSG). The file format of the imported model does not affect the 3D engine's representation of the BIM model. This is because after importing into a 3D engine, the model is expressed as a polygonal mesh regardless of the file format transmitted. It focuses only on mesh reconstruction of BIM models composed of triangle-based polygonal meshes used in 3D engines.

2-1. Mesh reconstruction method

The mesh reconstruction algorithm is designed to effectively and accurately generate a low-poly version of the 3D model after considering various properties of the BIM model used by the 3D engine. The proposed algorithm was extended using the basic QEM method developed by Garland and Heckbert. The overall process of the mesh reconstruction method is shown in Figure 3. The proposed approach only focuses on optimizing the mesh shape by extracting vertices and reconstructing the triangles of the mesh. The algorithm works according to the following steps:

(a) Use 3D engine properties to check the geometric mesh information of the imported original model and calculate the number of vertices and triangles in the model;

(b) Separate the shape information by extracting only the point cloud set (vertex) from the mesh composition information constituting the surface that matches the geometric and topological characteristics, excluding edges that cannot be shrunk.

(c) delete the original triangle mesh and check the deleted triangle mesh according to the curvature calculation and Quadrics error measurement methods;

(d) the set of extracted point clouds is reduced to existing vertices to generate new vertices by applying barycentric coordinate-based triangle centroid formula in 3D engine (e.g. Unity) code in C#;

(e) Adjust the reduction value (ex~90%). Repeat the process based on the set reduction value,

(f) If the reduction value is sufficiently satisfied, construct and reconstruct the triangle mesh with the newly created vertex information,

(g) Visualize and export the optimized model. A description of a sample form of the algorithm steps is shown in Figure 4.

Applying the proposed algorithm developed as a reconstruction technique based on the barycentric coordinate-based triangular center formula can significantly reduce the geometric information and data storage of the model. The proposed algorithm using the barycentric coordinate-based triangle center principle can merge at least three triangles into one triangle by calculating the average of the x-coordinate, y-coordinate, and z-coordinate of the triangle vertices. 2-2 explains in detail the formula used in the proposed method.

2-2. Triangle center formula based on barycentric coordinates

The center of the triangle is at the intersection of the triangle center lines, as shown in Figure 5. Let P1 P2 P3 be a triangle with vertices P1 P2 P3 in Euclidean n-space R ⁿ , and let C be the center of the triangle, as illustrated in Figure 5 for n=2. Then, C is given as the barycentric coordinate (S ₁ :S ₂ :S ₃ ) for the set {P1, P2, P3} by the following equation (see Equation 5 below).

[Formula 5]

Here, the center of gravity coordinates m1, m2, and m3 of P_3 are determined from Equation 9 below. The midpoint of sides P1 P2 is given as follows (see Equation 6 below).

[Formula 6]

Therefore, the equation of line L123 through points S(P1 P2) and P3 is as follows:

[Formula 7]

Line parameter t1 ∈R.

Line L123(t1) contains one of the three medians of triangle P1 P2 P3. Invoking circularity, the equations of lines L123, L231 and L312, each containing three triangle medians, are obtained from Equation 7 above by index circular permutation.

[Formula 8]

Here t1, t2,t3∈R.

In Figure 5, the triangle center C is the simultaneous point of the three lines in the above equation (Equation 8). This point of concurrency is determined by solving the following equation:

Here, L123(t1)=L231(t2)=L312(t3) becomes t1 t2=t3=2/3 for the unknowns t1, t2, t3∈R. Therefore, C is given by Equation 9 below.

[Formula 9]

Comparing Equation 9 and Equation 5, the special center of gravity coordinates (s1, s2, s3) of C for the set {P1, P2, P3} are given as s1=s2=s3=1/3. . Therefore, the convenient center of gravity coordinates (s1:s2:s3) of C can be given as Equation 10 below.

[Formula 10]

The proposed mesh reconstruction algorithm was redefined in script format to facilitate the development process and apply to the Unity 3D engine. Scripts are written in a specific language that Unity can understand, and the language used in Unity is called C# (C-sharp).

3. Experiment

Two model experiments were performed to verify the efficiency, reliability, and quality of the proposed method.

3-1. Testbed specifications and 3D modeling

Of the two test models, the first model is a steel-framed glass structure bridge (length = 34m, height = 5m, width = 2m) located between Engineering Building 1 23 and Engineering Building 2 25, and the second model is Engineering Building 2. . 2 Engineering Building. Both test models are from OOO University and were selected to demonstrate the feasibility of the proposed method. It was modeled in Autodesk Revit using existing two-dimensional drawings and exported to the Unity 3D engine using the Unity Reflect process. Unity Reflect can be used as a plug-in for Autodesk Revit, a BIM software, allowing models to be transferred from Revit to a real-time 3D experience. The modeling process for the two test models is shown in Figure 6.

3-2. Verification of bridge mesh reconstruction method

The proposed mesh reconstruction algorithm was tested with a bridge model to verify the algorithm especially for curved components. As shown in Figure 7, the entire bridge including various structures was optimized using the proposed method, and it can be confirmed that the shape of the entire model is absolutely the same. For comparison, we considered three items, including the number of vertices and triangles of the corresponding mesh and the file size of the model, which can intuitively indicate the reduction efficiency. Table 1 below shows the results of the proposed mesh reconstruction method for the test bridge model. The number of vertices and triangles was reduced by 56.7% and 67.9%, respectively, and the file size was reduced by 73.4%.

Table 1 shows the optimized percentage results for the vertices, triangles, and sizes of the model (bridge).

Model Category Original Optimized Improvement (%) SKKU Steel Bridge Number of vertices 590,942 255,625 ↓56.7 Number of triangles 335,564 107,573 ↓67.9 File size (KB) 50,535 13,451 ↓73.4

3-3. Verification of mesh reconstruction method for buildings

A building model was tested to verify the unrestricted performance of the proposed algorithm. Figure 8 shows that the proposed algorithm can successfully optimize the entire building and related facilities, and the overall shape of the building components did not change significantly after optimization. The model also considered three items for comparison, and the statistical results of the building comparison are shown in Table 2. The number of vertices and triangles was reduced by 46.6% and 58.6%, respectively, and the file size was also reduced by 66%.

Table 2 shows the optimized percentage results for the vertices, triangles, and sizes of the model (building).

Model Category Original Optimized Improvement (%) SKKU Engineering Building Number of vertices 4,183,198 2,235,879 ↓46.6 Number of triangles 3,231,090 1,336,165 ↓58.6 File size (KB) 364,761 124,173 ↓66

3-4. AR visualization testing of optimized models

After optimizing the model using the proposed method, both the original model and the optimized model were tested for visualization on the AR platform. As a result, as can be seen in the 3D engine as shown in Figures 9 and 10, not only the overall model but also the internal components are not visually significantly different. The results can be successfully imported into the AR device as shown in Figure 11.

4. Conclusion

Although BIM and AR technologies are being introduced in the AEC industry, there is a lack of devices that can efficiently transmit data from BIM to AR. Complex 3D models can increase computational effort while reducing and contributing to visualization efficiency and may require more storage space when using AR devices. Therefore, in this paper, we develop a 3D engine-based mesh reconstruction method for high-performance visualization when using AR devices. This new approach improves shape preservation of BIM models, reconstructing the models to the highest quality possible. The purpose of this study was to optimize the geometric information of the model using a mesh reconstruction algorithm to effectively visualize the BIM model with metadata and improve construction managers' decision-making and project processing efficiency. Two different types of models were used to evaluate the overall performance of the developed method. The proposed method can significantly optimize the BIM geometric model, reduce the file size of the model, and visualize the BIM model with high quality on AR devices.

In this study, issues affecting storage, transmission, and geometric data presentation when applying BIM to large-capacity AEC projects were addressed. The foundation of the solution for model storage optimization and display was built by managing a new information storage and architecture. This architecture includes 3D engine-based geometric optimization using a mesh reconstruction method that extends the original Quad-ric-Error-Metric (QEM) approach by leveraging barycentric coordinate-based triangular centroid formulas. Tests of the proposed method using two different types of large models were successfully performed and showed that the amount of stored data, meaning the file *?* size of the model, can be reduced by 73.4% and 66%, respectively, without affecting the quality. It was found that Visual performance. The main contribution to achieving this level was the improvements introduced to previous QEM-based mesh optimization methods with the introduction of surface segmentation, cumulative error measurements, and triangle mesh reconstruction. These improvements helped significantly reduce the number of triangles in the mesh while maintaining topology. The proposed method was successfully tested and displayed a very good quality model that significantly reduced the mesh number and could increase construction managers' decision-making and project processing efficiency.

For future work, more diverse and complex AEC model tests will also be performed to achieve better accuracy in mesh reconstruction. Additionally, not only the optimized model but also semantic data visualization can be experimented with AR devices for construction site management.

Due to the 3D engine-based geometric optimization method for visualizing building information models in augmented reality, the model is optimized by simplifying the geometric data by deleting the triangle mesh identified in the original model by excluding and reducing non-contractible edges in the original model. The efficiency of construction is improved, and the work efficiency of construction managers can be increased by reorganizing and optimizing large-capacity BIM models in mesh form and visualizing the optimized model without visual changes or errors.

In addition, since the optimization of the target 3D model is carried out while the shape of the structure is maintained, member deformation, member invasion, and combination and deletion phenomena between members may not occur, and distortion of the shape and size of each member of the original model may occur. It may not work. Through BIM model optimization, data can be efficiently compressed while showing a high level of agreement compared to the original model, and commercial smart devices can be actively used to manage construction site work, improving the efficiency of construction site management work. It can be.

Claims (5)

In a 3D engine-based geometric optimization method for visualizing building information models in augmented reality,
Confirming geometric mesh information of the original model loaded using 3D engine properties;
Confirming vertices and triangles in the original model;
Extracting and storing vertices from the original model;
Deleting the triangle mesh identified in the original model based on curvature calculation for boundary confirmation;
Setting a reduction value;
Reducing the vertices by creating new vertices by applying a triangular center formula based on barycentric coordinates; and
A step of reducing vertices to provide an optimized model,
The step of reducing the vertex is,
A set of extracted point clouds is created in a 3D engine such as Unity using C#,
After reducing the vertices, when the set vertex value is reached, reconstructing the triangle mesh using the generated vertex information; and
A 3D engine-based geometric optimization method for visualizing building information models in augmented reality, further comprising the step of storing reconstructed triangle mesh information and visualization information.
According to claim 1,
The step of extracting and storing vertices from the original model is,
A 3D engine-based geometric optimization method for building information model visualization in augmented reality, characterized by extracting from mesh composition information that constitutes a surface conforming to geometric and topological properties.
According to claim 1,
A 3D engine-based geometric optimization method for visualizing a building information model in augmented reality, further comprising the step of separating shape information by excluding non-contractible edges before deleting the triangle mesh identified in the original model.
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