Complex scene three-dimensional object matching method and device based on first-order distance-topology compatibility index

Abstract

The invention discloses a complex scene three-dimensional object matching method and device based on a first-order distance-topology compatibility index, and the method comprises the steps: firstly, designing a three-dimensional object first-order distance-topology compatibility index, and aims at achieving the expression of three-dimensional object positioning features; the geometric matching problem caused by the fact that a traditional first-order or second-order space compatibility index is difficult to process random position changes is solved; secondly, a three-dimensional object similarity calculation technology based on a first-order distance-topology compatibility index is provided, and the three-dimensional object similarity calculation technology is used for extracting an accurate matching pair and a potential matching pair; and finally, constructing a multi-sight-line constraint equation set by utilizing a relative position relationship between the precise matching pair and the potential matching pair, and realizing iterative self-correction of the spatial position of the BIM model in the potential matching pair. According to the method, high-precision matching of the real scenic spot cloud object and the BIM three-dimensional model in a complex scene is realized, and a basis is provided for automatic correction of the BIM model.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional object matching, and in particular to a method and device for matching three-dimensional objects in complex scenes based on a first-order distance-topological compatibility index. Background Art

[0002] As the digital economy enters a period of full expansion, traditional substations are gradually developing in the direction of digitalization and intelligence. Under this background trend, the construction of a substation digital twin system can display the power equipment of complex substations with a three-dimensional digital model, reflect the operating status of the power equipment in real time, form a virtual scene that is virtual and real with the real substation scene, and realize the intelligent perception, analysis, prediction and control of the substation in all directions and throughout the process, thereby enabling the construction of digital substations. However, there are differences between the real-world cloud model of the substation construction and operation stage and the three-dimensional model of the BIM equipment in the design stage, resulting in inconsistencies in the number, location, distribution and other aspects of the power equipment in the digital twin scene and the real scene. It is impossible to directly map the BIM model to the corresponding actual scene power equipment, which hinders the actual application of the BIM model in the digital twin of the substation. At present, the BIM model in the digital twin system is mainly matched and corrected manually, which is not only time-consuming and labor-intensive, but also easy to introduce human errors. Therefore, it is urgent to carry out research on automatic matching methods between three-dimensional objects in different scenes, to realize one-to-one mapping and matching of BIM equipment three-dimensional models to real-world point cloud objects, and to provide a basis for the automatic correction of BIM equipment three-dimensional models, which has important research significance and application value.

[0003] At present, the existing technologies for automatic matching between three-dimensional objects in different scenes mostly focus on matching research based on geometric consistency, taking the geometrically consistent overlapping areas between the scenes to be matched as references and capturing the same-name points to achieve efficient and accurate three-dimensional scene alignment. For example, there is a method that assumes that there is geometric consistency between the BIM scene and the real scene, thereby solving the matching of the real-life point cloud to the BIM space and the relocation of the mobile measurement target. However, the assumption of geometric consistency transformation such as rotation, scaling, and translation is not applicable to the target matching and spatial alignment problems of two types of data from different sources in most cases. Some recent studies have attempted to introduce the dynamic changes of the three-dimensional objects to be matched into the matching task between objects, mainly focusing on the movement, matching and relocation of objects in the three-dimensional scene. In addition, there are also some public data sets that use RGBD cameras or laser point clouds to generate three-dimensional matching tasks with dynamic change challenges. Although these data and methods take into account the geometric inconsistency between different scene data, they mainly adopt matching criteria based on the geometric shape similarity of three-dimensional objects; in the complex scenes of actual substations, there are a large number of power equipment with similar shapes but different types. It is necessary to make full use of the equipment type and its spatial neighbor information to improve the matching accuracy. It is difficult to directly apply these technologies to complete high-precision matching tasks.

[0004] The goal of traditional graph matching is to establish a one-to-one correspondence between nodes in two graphs. By constructing a graph network between three-dimensional objects, the matching of three-dimensional objects can be achieved using graph matching-based techniques. However, in complex substation scenarios, the matching between BIM equipment models and real-world point cloud objects belongs to a partial graph matching problem, and traditional graph matching methods are difficult to achieve high matching accuracy. Some recent studies have attempted to introduce deep learning methods into partial graph matching tasks, and proposed deep graph matching techniques for three-dimensional object matching tasks in changing scenes. For example, some classic graph network node encoding methods can achieve topological feature encoding of graph nodes through random walks of graph nodes, while SuperGlue and LightGlue based on deep learning can significantly enhance the topological positioning features of graph nodes through internal self-attention and cross-attention, and complete the matching task of BIM equipment models and real-world point cloud objects with high accuracy. Despite this, deep graph-based matching technology still faces a major problem, that is, the training of deep graph networks requires a large number of sample matching pairs, which to a certain extent requires manual annotation in complex scenes, resulting in a significant increase in training costs. Therefore, in order to reduce the mismatch between different categories of device objects and reduce the cost of 3D object matching tasks, it is necessary to utilize device objects with semantic labels, expand the spatial compatibility index of device graph network matching, and further improve the matching accuracy of 3D objects. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a three-dimensional object matching method based on first-order distance-topological similarity and adaptive correction of spatial position, which realizes high-precision matching between real-world point cloud objects and BIM three-dimensional models in complex scenes, and assists in the automatic correction of three-dimensional models of substation digital twin BIM equipment. The main contributions and innovations include: (1) a first-order distance-topological compatibility index for quantitative expression of three-dimensional object positioning features is designed, which can effectively alleviate the geometric matching problem caused by random position changes that traditional first-order or second-order spatial compatibility indexes are difficult to handle; (2) a three-dimensional object similarity calculation technology based on first-order distance-topological compatibility index is proposed, which aims to identify and extract accurate matching pairs and potential matching pairs, and provide a basis for the correction of three-dimensional object spatial position; (3) the idea of ​​forward reciprocation of photogrammetry is innovatively used to realize the automatic update of the spatial position of three-dimensional objects in potential matching pairs. The method proposed in the present invention is an iterative automatic matching and position update algorithm for real-world point cloud objects and BIM three-dimensional models, that is, the spatial position of the BIM model is continuously updated through iterative matching until the number of accurate matching pairs remains relatively stable. The method of the present invention was experimented on a three-dimensional data set of a complex substation. By comparing with the existing benchmark method, the experimental results showed the effectiveness and reliability of the method of the present invention.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A first aspect provides a complex scene three-dimensional object matching method based on a first-order distance-topological compatibility index, comprising:

[0008] According to the relative distance and spatial topological positioning characteristics between three-dimensional objects, a first-order distance-topological compatibility index of three-dimensional objects is constructed;

[0009] According to the spatial layout of complex three-dimensional objects, the first-order distance-topological compatibility index between the real-world point cloud objects and the BIM objects is calculated respectively;

[0010] Based on the first-order distance-topological compatibility index of the real-world point cloud object and the BIM object, the first-order distance similarity and spatial topological similarity between the BIM object and the real-world point cloud object are calculated;

[0011] The initial matching pairs are generated by maximizing the first-order distance similarity;

[0012] The exact matching pairs are established by maximizing the first-order topological neighbor similarity;

[0013] Potential matching pairs are searched based on the first-order neighbor objects of the exact matching pairs, and the spatial positions of the BIM objects are adaptively updated.

[0014] In one embodiment, a first-order distance-topological compatibility index of three-dimensional objects is constructed based on the relative distance between the three-dimensional objects and the spatial topological positioning features, including:

[0015] Calculate the geometric distance between any two three-dimensional objects and obtain the distance matrix;

[0016] Traverse each row of the distance matrix, and express the relationship between the corresponding 3D object and the other 3D objects in the scene in turn with the key-value pair of "type-distance", so as to complete the generation of the first-order geometric distance positioning feature vector of the 3D object in the global space;

[0017] The topological relationship of the three-dimensional objects in the scene is established based on the three-dimensional object Voronoi diagram, and the type labels of the neighboring objects of the three-dimensional objects are counted to generate a type label histogram, and the first-order topological feature vector is obtained as the first-order topological positioning feature expression;

[0018] The first-order geometric distance positioning feature vector and the first-order topological feature vector are used as the first-order distance-topological compatibility index of three-dimensional objects.

[0019] In one implementation, the first-order geometric distance positioning feature vector is calculated as follows:

[0020]

[0021] Among them, i and j represent two three-dimensional objects, D i→j Indicates the geometric distance from 3D object i to j in the scene, C M-1 Indicates the device type of the M-1th 3D object, where M indicates the number of 3D objects in the scene. Represents the first-order distance feature vector of three-dimensional object i;

[0022] The first-order topological eigenvector is calculated as:

[0023]

[0024] Among them, U i represents the first-order topological neighbor set of three-dimensional object i, q represents the first-order topological neighbor of three-dimensional object i, C q represents the type of the first-order topological neighbors of a three-dimensional object i, Indicates that the neighbor type of the three-dimensional object i is C j The number of represents the first-order topological feature vector of three-dimensional object i, and k represents a three-dimensional object.

[0025] In one embodiment, based on the first-order distance-topology compatibility index of the real-world point cloud object and the BIM object, the first-order distance similarity between the BIM object and the real-world point cloud object is calculated, including:

[0026] For the real-time point cloud object s, use its equipment type as a filter tag, obtain BIM objects with the same equipment type, and add them to the matching object set;

[0027] For the current real-life scenic spot cloud object s and BIM object t, the minimum distance difference is calculated dimension by dimension. Specifically, each key-value pair in the first-order distance feature vector of the real-life scenic spot cloud object s is selected in turn, and the key-value pair set with the same device type in the first-order distance feature vector of the BIM object t is filtered by the device type of the key-value pair. The difference between the distance in the current key-value pair and each distance in the filtered key-value pair set is calculated respectively, and the minimum value of the distance difference is taken as the distance between the real-life scenic spot cloud object s and the BIM object t in the current dimension.

[0028] Sum the minimum distance differences between the real-world point cloud object s and the BIM object t in all dimensions to obtain the similarity between them in the first-order distance feature, which is used as the first-order distance similarity between the BIM object and the real-world point cloud object;

[0029] For the real-world point cloud object s and the BIM object t, the spatial topological similarity between the BIM object and the real-world point cloud object is obtained by calculating the quantitative differences in the corresponding dimensions of the first-order topological feature vectors of the two objects and summing up the differences in all dimensions.

[0030] In one embodiment, the calculation formula for the first-order distance similarity between the BIM object and the real-world point cloud object is:

[0031]

[0032] in, It represents the distance difference between the mth dimension of the real-time point cloud object s and the nth dimension of the BIM object t after filtering the same type of key-value pairs, D s→m Indicates the distance value on the mth dimension in the real-time point cloud object s, D t→n represents the distance value in the nth dimension of BIM object t, represents the minimum distance difference between the real-world point cloud object s and the BIM object t in the mth dimension, Δd s→t It represents the first-order distance similarity between the real-world point cloud object s and the BIM object t, and represents the similarity between the two objects in the first-order geometric distance feature;

[0033] The calculation formula of the spatial topological similarity between BIM objects and real-world point cloud objects is:

[0034]

[0035] and The topological neighbors of the real-time point cloud object s and the BIM object t belong to type C q The number of neighbors of Indicates that in type C q The difference in the number of topological neighbors between the two objects, Δn s→t Represents the spatial topological similarity between the real-world point cloud object s and the BIM object t.

[0036] In one embodiment, an exact matching pair is established by maximizing the first-order topological neighbor similarity, including:

[0037] By maximizing the first-order topological neighbor similarity, the pairs with the largest first-order topological neighbor similarity and the smallest first-order geometric distance are selected from the initial matching pairs as the exact matching pairs.

[0038] In one embodiment, searching for potential matching pairs based on first-order neighbor objects of exact matching pairs and adaptively updating the spatial positions of BIM objects includes:

[0039] Select potential matching pairs from the first-order neighbors of the exact matching pairs, requiring that the neighbors of the potential matching pairs contain exact matching objects and are exact matching pairs of each other;

[0040] Taking the distance relationship between all accurate matching pairs and potential matching pairs as the constraint condition, a multi-line of sight constraint equations is established to achieve adaptive correction of the spatial position of BIM objects in potential matching pairs.

[0041] Based on the same inventive concept, the second aspect of the present invention provides a complex scene three-dimensional object matching device based on a first-order distance-topological compatibility index, comprising:

[0042] An index construction module is used to construct a first-order distance-topological compatibility index of three-dimensional objects based on the relative distance between the three-dimensional objects and the spatial topological positioning features;

[0043] An index calculation module is used to calculate the first-order distance-topological compatibility index of the real-world point cloud object and the BIM object respectively according to the spatial layout of the complex three-dimensional object;

[0044] A similarity calculation module is used to calculate the first-order distance similarity and spatial topological similarity between the BIM object and the real-world point cloud object based on the first-order distance-topological compatibility index of the real-world point cloud object and the BIM object;

[0045] An initial matching pair generation module is used to generate an initial matching pair by maximizing the first-order distance similarity;

[0046] An accurate matching pair establishment module is used to establish accurate matching pairs by maximizing the first-order topological neighbor similarity;

[0047] Adaptive correction module, used to search for potential matching pairs based on the first-order neighbor objects of the exact matching pairs, and adaptively update the spatial position of the BIM objects

[0048] Based on the same inventive concept, the third aspect of the present invention provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for matching three-dimensional objects in complex scenes based on the first-order distance-topological compatibility index described in the first aspect is implemented.

[0049] Based on the same inventive concept, the fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and run on the processor. When the processor executes the program, the complex scene three-dimensional object matching method based on the first-order distance-topological compatibility index described in the first aspect is implemented.

[0050] Compared with the prior art, the advantages and beneficial technical effects of the present invention are as follows:

[0051] The present invention proposes a complex scene three-dimensional object matching method based on the first-order distance-topological compatibility index. First, the first-order distance-topological compatibility index of the three-dimensional object is constructed to achieve the expression of the positioning characteristics of the three-dimensional object, thereby alleviating the problem of geometric matching caused by random position changes that the traditional first-order or second-order spatial compatibility index is difficult to handle; and a calculation technology for the similarity of three-dimensional objects based on the first-order distance-topological compatibility index is proposed to extract accurate matching pairs and potential matching pairs; finally, the relative position relationship between the accurate matching pairs and the potential matching pairs is used to construct a multi-line of sight constraint equations to achieve iterative self-correction of the spatial position of the BIM model in the potential matching pairs, thereby achieving accurate matching of three-dimensional objects. The method of the present invention was experimented on a complex scene three-dimensional data set, and the overall matching accuracy reached 97.92%. The experimental results show that compared with the current benchmark method, the method of the present invention has higher accuracy and reliability in the complex scene three-dimensional object matching task. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 It is an overall flow chart of a complex scene three-dimensional object matching method based on a first-order distance-topological compatibility index in an embodiment of the present invention;

[0054] Figure 2 It is a flowchart of calculating the first-order distance-topological similarity between the real-world point cloud object and the BIM object in an embodiment of the present invention;

[0055] Figure 3 Schematic diagram of adaptive correction of spatial position of three-dimensional object in an embodiment of the present invention;

[0056] Figure 4 is the experimental data set in the embodiment of the present invention, where (a) is the single device distribution of the real-time point cloud scene, and (b) is the single device distribution of the BIM scene;

[0057] Figure 5 Result diagrams of BIM three-dimensional object matching during the iteration process of an embodiment of the present invention: (a) is a matching result diagram when the number of iterations is 1; (b) is a matching result diagram when the number of iterations is 3; (c) is a matching result diagram when the number of iterations is 5; (d) is a matching result diagram when the number of iterations is 7; (e) is a diagram showing the change in the percentage of three-dimensional object matching in a BIM scene with the number of iterations; in the diagram, green, red, and gray represent correctly matched, incorrectly matched, and unused BIM three-dimensional objects, respectively;

[0058] Figure 6 This is the spatial distribution diagram of BIM 3D object matching results in a complex substation scenario. In the figure, green, blue, red, and orange represent correctly matched, correctly deleted, incorrectly matched, and incorrectly deleted BIM 3D objects, respectively;

[0059] Figure 7 Comparison of the spatial distribution of BIM 3D object matching effects under different methods in a complex substation scene: (a) FGM; (b) DGM; (c) DeepWalk-LightGlue; (d) GraRep-LightGlue; (e) NetMF-LightGlue; (f) Node2Vec-LightGlue; (g) NodeSketch-LightGlue; (h) the method of the present invention;

[0060] Figure 8 FIG. 4 is a graph showing how the overall accuracy of different methods varies with the size of the displacement noise in a specific implementation manner. DETAILED DESCRIPTION

[0061] In view of the differences between the real-world scenic spot cloud model of complex construction scenes and the BIM three-dimensional model in the design stage, which leads to inconsistencies in the number, position, and distribution of three-dimensional objects in different scenes, hindering the practical application of BIM models in digital twins, this paper proposes a three-dimensional object matching method based on first-order distance-topological similarity and spatial position adaptive correction, which realizes high-precision matching between real-world scenic spot cloud objects and BIM three-dimensional models in complex scenes, and provides a basis for the automatic correction of BIM models. Firstly, a first-order distance-topological compatibility index of three-dimensional objects is designed to express the positioning characteristics of three-dimensional objects, thereby alleviating the problem of geometric matching caused by random position changes that traditional first-order or second-order spatial compatibility indexes are difficult to handle; secondly, a three-dimensional object similarity calculation technology based on the first-order distance-topological compatibility index is proposed to extract accurate matching pairs and potential matching pairs; finally, the relative position relationship between accurate matching pairs and potential matching pairs is used to construct a multi-line of sight constraint equations to realize iterative self-correction of the spatial position of BIM models in potential matching pairs.

[0062] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Embodiment 1

[0064] The present invention discloses a complex scene three-dimensional object matching method based on a first-order distance-topology compatibility index, comprising:

[0065] S1: Construct the first-order distance-topological compatibility index of three-dimensional objects based on the relative distance and spatial topological positioning features between three-dimensional objects;

[0066] S2: According to the spatial layout of complex 3D objects, the first-order distance-topological compatibility index between the real-world point cloud objects and the BIM objects is calculated respectively;

[0067] S3: Calculate the first-order distance similarity and spatial topological similarity between the BIM object and the real-world point cloud object based on the first-order distance-topological compatibility index between the real-world point cloud object and the BIM object;

[0068] S4: Generate initial matching pairs by maximizing the first-order distance similarity;

[0069] S5: Establish accurate matching pairs by maximizing the first-order topological neighbor similarity;

[0070] S6: Search for potential matching pairs based on the first-order neighbor objects of the exact matching pairs, and adaptively update the spatial position of the BIM objects.

[0071] Specifically, the present invention proposes a spatial position iterative correction three-dimensional object matching method based on the first-order distance-topological compatibility index, which generates positioning feature descriptors by utilizing the semantic labels and spatial relationships between the objects to be matched, and achieves high-precision matching between BIM objects and real-life point cloud objects in complex scenes by iteratively updating the spatial positions of the three-dimensional objects.

[0072] Specifically, if Figure 1 As shown, the method of the present invention can be implemented by a calculation process, which mainly includes the following steps:

[0073] Step 1: Define the first-order distance-topological compatibility index of the three-dimensional object to describe the positioning characteristics of the three-dimensional object

[0074] Step 2: According to the spatial layout of complex three-dimensional objects, the first-order distance-topological compatibility index of the real-world point cloud objects and BIM objects is calculated respectively;

[0075] Step 3: Based on the first-order distance-topological compatibility index of the three-dimensional object, calculate the first-order distance similarity and spatial topological similarity between the BIM object and the real-world point cloud object;

[0076] Step 4: Generate an initial matching pair, that is, use the method of maximizing the first-order distance similarity to match the real-time point cloud object to the BIM object with the smallest first-order distance in turn. If the current BIM object with the smallest first-order distance is matched to another real-time point cloud object, then the BIM object with the second smallest first-order distance will be matched until the initial matching is completed;

[0077] Step 5: Establish an exact matching pair, that is, adopt the method of maximizing the first-order topological neighbor similarity, and select the ones with the largest first-order topological neighbor similarity and the smallest first-order geometric distance from the initial matching pairs as the exact matching pair;

[0078] Step 6: Search for potential matching pairs and adaptively update the spatial position of BIM objects, that is, select potential matching pairs from the first-order neighbors of exact matching pairs, requiring that the neighbors of the potential matching pairs contain exact matching objects and are exact matching pairs of each other. On this basis, take the distance relationship between all exact matching pairs and potential matching pairs as the constraint condition, establish a multi-view constraint equation group, and realize the adaptive correction of the spatial position of BIM objects in potential matching pairs.

[0079] The above steps 2 to 6 are repeated until the number of exact matching pairs reaches a stable level, and the iteration is terminated.

[0080] The key contents of the present invention include the first-order distance-topological compatibility index of three-dimensional objects, the calculation of three-dimensional object matching similarity, and the adaptive correction of the spatial position of three-dimensional objects. The three aspects are described in detail below.

[0081] 1.1 First-order distance of three-dimensional objects - topological compatibility index

[0082] According to the relative distance and spatial topological positioning features between three-dimensional objects, the first-order distance-topological compatibility index of three-dimensional objects is constructed, which can be achieved in the following ways:

[0083] Calculate the geometric distance between any two three-dimensional objects and obtain the distance matrix;

[0084] Traverse each row of the distance matrix, and express the relationship between the corresponding 3D object and the other 3D objects in the scene in turn with the key-value pair of "type-distance", so as to complete the generation of the first-order geometric distance positioning feature vector of the 3D object in the global space;

[0085] The topological relationship of the three-dimensional objects in the scene is established based on the three-dimensional object Voronoi diagram, and the type labels of the neighboring objects of the three-dimensional objects are counted to generate a type label histogram, and the first-order topological feature vector is obtained as the first-order topological positioning feature expression;

[0086] The first-order geometric distance positioning feature vector and the first-order topological feature vector are used as the first-order distance-topological compatibility index of three-dimensional objects.

[0087] Specifically, the relative distance between a three-dimensional object and other objects in a spatial scene can effectively describe its orientation information, and is one of the geometric expression features for matching three-dimensional objects. Such features can usually be expressed using first-order or second-order spatial compatibility indicators. Traditional methods can solve geometric matching problems caused by transformations such as translation and rotation, but cannot effectively deal with geometric matching problems caused by changes in relative spatial layout due to random position offsets. To this end, the present invention, based on the expression of first-order spatial compatibility positioning features, introduces spatial topological positioning features, and proposes a first-order distance-topological compatibility index for three-dimensional objects, thereby effectively dealing with geometric transformation problems caused by random position changes, and facilitating high-precision geometric matching between real-world point cloud objects and BIM objects in complex substation scenarios.

[0088] During the specific implementation process, it is assumed that there are M three-dimensional objects in the scene. This embodiment method provides a calculation process for the first-order distance-topological compatibility index of the three-dimensional objects. First, the geometric distance between any two three-dimensional objects is calculated to obtain a distance matrix. Secondly, each row of the distance matrix is ​​traversed, and the relationship between the corresponding three-dimensional object L and the remaining M-1 three-dimensional objects in the scene is expressed in turn using the key-value pair of "type-distance", thereby completing the generation of the first-order geometric distance positioning feature vector of the three-dimensional object L in the global space. As shown in formula (1), in the formula, i and j represent two three-dimensional objects respectively, D i→j Indicates the geometric distance from 3D object i to j in the scene, C M-1 Indicates the device type of the M-1th 3D object, F i Geoi Represents the first-order distance feature vector of three-dimensional object i. Third, construct the Voronoi diagram of the three-dimensional object

[20] , establish the topological relationship of the three-dimensional objects in the scene, and count the type labels of the neighboring objects of the three-dimensional objects to generate a type label histogram as the first-order topological positioning feature expression. As shown in formula (2), U i represents the first-order topological neighbor set of three-dimensional object i, Indicates that the neighbor type of the three-dimensional object i is C j The number of Represents the first-order topological eigenvector of three-dimensional object i.

[0089]

[0090] 1.2 3D Object Matching Similarity Calculation

[0091] The feature similarity between three-dimensional objects is an important basis for object matching. Traditional feature similarity algorithms require the consistency of the feature dimensions of two objects. However, in complex substation scenarios, it is difficult to ensure that the first-order distance features and topological features of two three-dimensional objects have the same feature dimensions. Therefore, this implementation method uses the minimum distance difference sum method to estimate the similarity of the first-order distance features, and uses the topological neighbor type statistical histogram difference method to estimate the similarity of the first-order topological features.

[0092] like Figure 2 As shown, since the number of real-life point cloud objects is inconsistent with the number of BIM objects, this embodiment assumes that a relatively small number of real-life point clouds are used to calculate the similarity between each real-life point cloud object and each BIM object.

[0093] First, for the real-time point cloud object s, use its device type as the filtering label to quickly obtain BIM objects with the same device type and add them to the set of matching objects; second, for the current real-time point cloud object s and BIM object t, calculate the minimum distance difference dimension by dimension, that is, select each key-value pair in the first-order distance feature vector of the real-time point cloud object s in turn, and use the device type of the key-value pair to filter the key-value pair set with the same device type in the first-order distance feature vector of BIM object t, and calculate the difference between the distance in the current key-value pair and each distance in the filtered key-value pair set, and use the minimum value of the distance difference as the distance between the real-time point cloud object s and BIM object t in the current dimension; third, sum the minimum distance differences between the real-time point cloud object s and BIM object t in all dimensions to obtain the similarity between them in the first-order distance feature. As shown in formula (3), It represents the distance difference between the mth dimension of the real-time point cloud object s and the nth dimension of the BIM object t after filtering the same type of key-value pairs, D s→m Indicates the distance value on the mth dimension in the real-time point cloud object s, D t→n represents the distance value in the nth dimension of BIM object t, represents the minimum distance difference between the real-world point cloud object s and the BIM object t in the mth dimension, Δd s→t Represents the similarity between the real-world point cloud object s and the BIM object t in the first-order geometric distance feature.

[0094] Similar to the calculation strategy of the first-order distance feature similarity, the calculation of the first-order topological feature similarity is also performed between objects with the same type of labels. Specifically, for the real-world cloud object s and the BIM object t, this embodiment calculates the quantitative difference in the corresponding dimensions of the first-order topological feature vectors of the two objects and sums the differences in all dimensions to obtain the first-order topological feature similarity between the two objects. Its essence is the difference in the statistical histogram of the topological neighbor types between the two objects. As shown in formula (4), and The topological neighbors of the real-time point cloud object s and the BIM object t belong to type C q The number of neighbors of Indicates that in type C q The difference in the number of topological neighbors between the two objects, Δn s→t Represents the first-order topological feature similarity between the real-world point cloud object s and the BIM object t.

[0095]

[0096] 1.3 Adaptive correction of 3D object spatial position

[0097] The exact match pair is generated when the real-time point cloud object and the BIM object strictly meet the conditions of maximum first-order topological neighbor similarity and minimum first-order geometric distance. It is the most likely match pair between the real-time point cloud and the BIM scene, and their relative position and spatial distribution have high stability. In addition, given that the topological neighbor type statistical histograms of the exact match pairs are exactly the same, the first-order topological neighbors of the exact match pairs are more likely to become potential match pairs, that is, the potential match pairs have exact match objects in their respective neighbors and are exact match pairs with each other. However, if Figure 3 As shown, the relative positions of potential matching pairs (blue five-pointed star symbols) may have random position deviations. Therefore, the accurate matching pairs can be used as a control for the position correction of potential matching pairs, so as to generate more accurate matching pairs in the iterative process of object matching.

[0098] After several potential matching pairs are determined through precise matching pairs in the scene, the spatial positions of the potential matching pairs can be recalculated and updated using the multi-point positioning method. The algorithm diagram for self-correction of the potential matching pair positions is shown in the figure below: Figure 3 As shown in the figure, the main idea is based on the idea of ​​forward intersection based on photogrammetry, that is, taking the spatial position of the accurately matched 3D object in the BIM scene as the center of the circle, and the spatial distance from the accurately matched 3D object in the real-life cloud scene to its potential matching 3D object as the radius, using the forward intersection technology, a multi-view constraint equation group is established, and combined with the least squares principle, the spatial position of the corresponding potential matching 3D object in the BIM scene is determined, thereby obtaining its position correction vector, and then realizing the automatic update of the position of the potential matching object during the matching iteration process. The specific calculation formula is shown in (5), which gives the multi-view constraint equation group, where, and They represent the coordinates of the potential matching pair L in the real-time cloud scene and the BIM scene, n represents the number of accurate matching pairs in the current iteration process, and Respectively represent the coordinates of the first accurate matching pair in the real-time point cloud scene and the BIM scene. and Under known conditions, the least squares technique is used to solve Then we get the position correction vector (i.e. This enables automatic correction of the spatial position of potential three-dimensional objects.

[0099]

[0100] The effectiveness and reliability of the method proposed in the present invention are verified through specific experiments below.

[0101] 2.1 Experimental Dataset and Model Evaluation

[0102] In order to verify the effectiveness and reliability of the method proposed in this invention, a complex substation scene was selected to carry out experiments. On the one hand, the real-life point cloud data of the substation was obtained using a ground-based laser scanner to reflect the three-dimensional equipment spatial layout during the actual operation of the substation; on the other hand, based on the substation digital twin platform, the BIM three-dimensional model data of the equipment running on the platform was obtained. The real-life three-dimensional point cloud obtained by the ground-based laser scanner contains a large number of spatially continuous three-dimensional points. First, the ground three-dimensional points need to be removed through a cloth algorithm, and then they are segmented to obtain multiple independent individual devices. On this basis, a type label is assigned to each device. Figure 4 Part (a) shows the 3D point clouds of different types of equipment obtained by segmenting the real-world point cloud. The BIM scene contains 180 equipment 3D models, each of which corresponds to a single BIM 3D model with detailed equipment type information and is stored in the fbx file type. The present invention first performs spatial uniform sampling on the BIM 3D model to obtain the BIM point cloud of the single equipment, then calculates the geometric center and stores it in the form of "object ID-type label-geometric center coordinates". Figure 4 Part (b) shows the 3D point clouds of different types of equipment in the BIM scene.

[0103] In the embodiment, the overall accuracy index (OA) is used to quantitatively evaluate the matching accuracy of all devices in the BIM scene. As shown in formula (6), OA represents the overall accuracy of BIM three-dimensional object matching, Indicates the number of correctly matched BIM 3D objects. Indicates the number of BIM 3D objects that are correctly deleted. Indicates the number of incorrectly matched BIM 3D objects. Indicates the number of BIM 3D objects that were mistakenly deleted. It should be noted that, considering the possible inconsistency in the number of equipment objects between the real-time point cloud scene and the BIM scene, when calculating the BIM object matching accuracy, it is necessary to consider the correctly matched BIM objects, correctly deleted BIM objects, incorrectly matched BIM objects, and incorrectly deleted BIM objects at the same time.

[0104]

[0105] 2.2 Experimental Results Analysis

[0106] Figure 5(a)-(d) respectively show the specific matching process of the real-life point cloud 3D objects and the BIM 3D objects in the 1st, 3rd, 5th and 7th iterations of the method of the present invention. It can be clearly seen that with the increase in the number of iterations, more and more real-life point cloud 3D objects establish precise matching relationships with the corresponding BIM 3D objects. Finally, the matching was completed after 7 iterations, and the overall matching accuracy reached 97.92%. In the result graph of each iteration, the green point cloud object represents the correct matching pair in the current iteration process, the red point cloud object represents the incorrect matching pair in the current iteration process, and the gray point cloud object represents the 3D object that has not yet been matched in the current iteration process. Figure 5 As shown in (e), when the number of iterations increases from 1 to 7, the percentage of correctly matched BIM 3D objects in the total BIM number increases from 37.78% to 59.44%, and the percentage of incorrectly matched BIM 3D objects in the total BIM number is no more than 1.11%, while the percentages of correctly deleted and incorrectly deleted BIM 3D objects are 38.33% and 1.11%, respectively. This result shows that through iterative matching and adaptive position updating, this method can accurately complete the 3D object matching of complex substation scenes, showing its effectiveness and accuracy.

[0107] In order to demonstrate the matching effect of 3D objects in complex substation scenes, Figure 6 The spatial superposition result diagram of the real-world point cloud object and the BIM 3D object is drawn in different colors, where green represents correctly matched BIM 3D objects, blue represents correctly deleted BIM 3D objects, red represents incorrectly deleted BIM 3D objects, and orange represents incorrectly matched BIM 3D objects. It can be seen that only 4 BIM 3D objects are incorrectly matched and deleted, which further proves the effectiveness of the algorithm of the present invention.

[0108] 2.3 Comparative analysis of experimental methods

[0109] To further verify the effectiveness of the method of the present invention, this embodiment method carried out comparative analysis experiments with benchmark methods, including two traditional graph network matching methods such as Factorized Graph Matching (FGM) and Deformable Graph Matching (DGM) and five deep learning matching methods. Due to the differences in graph node feature encoders, this implementation method uses DeepWalk, GraRep, NetMF, Node2Vec and NodeSketch as feature encoders of graph nodes in turn, and uses the latest deep learning matching algorithm LightGlue to complete the matching of real-world point cloud objects and BIM three-dimensional objects. Therefore, the deep learning matching method includes five different types, including DeepWalk-LightGlue, GraRep-LightGlue, NetMF-LightGlue, Node2Vec-LightGlue, and NodeSketch-LightGlue.

[0110] The comparative experimental results of different methods are shown in Table 1. It can be seen that the matching accuracy of the traditional graph network matching method is significantly lower than that of the method of the present invention and the method based on deep learning, which shows the limitations of the traditional graph network matching method in dealing with complex substation three-dimensional object matching tasks; while the matching accuracy of the method of the present invention and the method based on deep learning is more than 90%, which shows the effectiveness of these methods in the three-dimensional object matching task of complex substation scenes. Specifically, the matching accuracy of the method of the present invention is the highest, reaching 97.92%, while the matching accuracy of the DGM method is the worst, only 54.67%; among the matching methods based on deep learning, the matching accuracy of NetMF-LightGlue is relatively high, reaching 95.81%, while the matching accuracy of NodeSketch-LightGlue is relatively poor, at 92.39%. In particular, the proposed method improves the accuracy by 39.44%, 43.25%, 3.80%, 3.46%, 2.11%, 4.15% and 5.53% over FGM, DGM, DeepWalk-LightGlue, GraRep-LightGlue, NetMF-LightGlue, Node2Vec-LightGlue and NodeSketch-LightGlue, respectively. Figure 7 The matching effects of BIM 3D objects under different methods in a complex substation scene are demonstrated, where green and blue represent correctly matched and correctly deleted BIM 3D objects, respectively, and red and orange represent incorrectly matched and incorrectly deleted BIM 3D objects, respectively. These matching effects intuitively show the effectiveness of this method over the comparison method.

[0111] Table 1 Overall accuracy comparison between the proposed method and existing methods in the literature on 3D object matching in complex substation scenes

[0112]

[0113] 2.4 Sensitivity analysis of experimental methods

[0114] In order to further verify the reliability of the method of the present invention, this embodiment constructs a three-dimensional object dataset with different noise levels and conducts sensitivity analysis of different methods. By adding different levels of random displacement noise to the center point coordinates of the real-life point cloud three-dimensional object and the BIM three-dimensional object, a three-dimensional object dataset with random position offset is formed to verify the reliability of different methods on more complex three-dimensional object datasets. In particular, for each three-dimensional object, with its center point as the circle point and the north direction as the starting direction, a uniformly distributed offset angle between 0-360 degrees is randomly generated, and an offset length conforming to the normal distribution Norm (r, 1) is further randomly generated, and finally the three-dimensional object is randomly translated according to the offset angle and the offset length. In this experiment, the value of r is set to 0.2 meters, 0.4 meters, 0.6 meters, 0.8 meters, 1.0 meters and 1.2 meters, thereby obtaining 6 groups of real-life point cloud three-dimensional object datasets and BIM three-dimensional object datasets with different noise levels.

[0115] The experimental results under different noise levels are shown in Figure 8 As shown, it can be seen that for most noisy 3D data sets, the method of the present invention can achieve the highest overall accuracy, that is, when r is 0.2m, 0.4m, 0.6m, 0.8m, 1.0m and 1.2m, the overall accuracy of the method of the present invention is 97.92%, 96.89%, 90.66%, 92.73%, 92.39% and 93.08% respectively. Among the matching methods based on deep learning, the overall accuracy of NodeSketch-LightGlue always performs relatively poorly; while among the matching methods based on traditional graph networks, the overall accuracy of FGM and DGM is significantly lower than 50% after the r value is higher than 0.4m, and it always performs the worst. Specifically, with the increase of r value, the overall accuracy of the method of the present invention and other comparison methods shows a generally downward trend, however, the overall accuracy of the method of the present invention is always higher than 90%; when r is 1.2 meters, the overall accuracy of the method of the present invention is 4.84%, 2.08%, 3.11%, 4.15% and 10.38% higher than that of DeepWalk-LightGlue, GraRep-LightGlue, NetMF-LightGlue, Node2Vec-LightGlue and NodeSketch-LightGlue based on deep learning, respectively, demonstrating the reliability of the method of the present invention in matching three-dimensional objects in complex substations.

[0116] In general, the present invention proposes a high-precision matching method between real-world point cloud 3D objects and BIM 3D objects in complex scenes, designs a 3D object positioning feature expression model based on the first-order distance-topological compatibility index, constructs a similarity measurement calculation technology based on 3D object type labels, and uses the idea of ​​forward intersection of photogrammetry to iteratively update the spatial position of BIM 3D objects through adaptive calculation, thereby achieving effective and reliable matching between 3D objects of different modalities in complex scenes, and solving the problems of inconsistent quantity, different geometric positions, and inconsistent spatial relationships between BIM 3D objects and actual running objects in the digital twin platform. The method of the present invention is experimentally compared with the existing 3D object space matching method, showing that the method of the present invention has a high overall object matching accuracy, proving the effectiveness of the method of the present invention in the task of 3D object matching in complex scenes; by constructing 3D object data sets with different noise levels, the stability of the method of the present invention in the overall object matching accuracy is verified, further proving the reliability of the method of the present invention in the task of 3D object matching in complex scenes.

[0117] Embodiment 2

[0118] Based on the same inventive concept, this embodiment discloses a complex scene three-dimensional object matching device based on a first-order distance-topological compatibility index, comprising:

[0119] An index construction module is used to construct a first-order distance-topological compatibility index of three-dimensional objects based on the relative distance between the three-dimensional objects and the spatial topological positioning features;

[0120] An index calculation module is used to calculate the first-order distance-topological compatibility index of the real-world point cloud object and the BIM object respectively according to the spatial layout of the complex three-dimensional object;

[0121] A similarity calculation module is used to calculate the first-order distance similarity and spatial topological similarity between the BIM object and the real-world point cloud object based on the first-order distance-topological compatibility index of the real-world point cloud object and the BIM object;

[0122] An initial matching pair generation module is used to generate an initial matching pair by maximizing the first-order distance similarity;

[0123] An accurate matching pair establishment module is used to establish accurate matching pairs by maximizing the first-order topological neighbor similarity;

[0124] The adaptive correction module is used to search for potential matching pairs based on the first-order neighbor objects of the exact matching pairs and adaptively update the spatial position of the BIM objects.

[0125] Since the device introduced in the second embodiment of the present invention is a device used to implement the complex scene three-dimensional object matching method based on the first-order distance-topological compatibility index in the first embodiment of the present invention, based on the method introduced in the first embodiment of the present invention, the person skilled in the art can understand the specific structure and deformation of the device, so it is not repeated here. All devices used in the method in the first embodiment of the present invention belong to the scope of protection of the present invention.

[0126] Embodiment 3

[0127] Based on the same inventive concept, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the first embodiment is implemented.

[0128] Since the computer-readable storage medium introduced in the third embodiment of the present invention is a computer-readable storage medium used to implement the complex scene three-dimensional object matching method based on the first-order distance-topological compatibility index in the first embodiment of the present invention, based on the method introduced in the first embodiment of the present invention, the person skilled in the art can understand the specific structure and deformation of the computer-readable storage medium, so it is not repeated here. All computer-readable storage media used in the method of the first embodiment of the present invention belong to the scope of protection of the present invention.

[0129] Embodiment 4

[0130] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in Embodiment 1 when executing the program.

[0131] Since the computer device introduced in the fourth embodiment of the present invention is a computer device used to implement the complex scene three-dimensional object matching method based on the first-order distance-topological compatibility index in the first embodiment of the present invention, based on the method introduced in the first embodiment of the present invention, the person skilled in the art can understand the specific structure and deformation of the computer device, so it is not repeated here. All computer devices used in the method of the first embodiment of the present invention belong to the scope of protection of the present invention.

[0132] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0133] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic creative concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the embodiments of the present invention without departing from the spirit and scope of the embodiments of the present invention. Thus, if these modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A complex scene three-dimensional object matching method based on first-order distance-topological compatibility index, characterized in that: include: According to the relative distance and spatial topological positioning characteristics between three-dimensional objects, a first-order distance-topological compatibility index of three-dimensional objects is constructed; According to the spatial layout of complex three-dimensional objects, the first-order distance-topological compatibility index between the real-world point cloud objects and the BIM objects is calculated respectively; Based on the first-order distance-topological compatibility index of the real-world point cloud object and the BIM object, the first-order distance similarity and spatial topological similarity between the BIM object and the real-world point cloud object are calculated; The initial matching pairs are generated by maximizing the first-order distance similarity; The exact matching pairs are established by maximizing the first-order topological neighbor similarity; Potential matching pairs are searched based on the first-order neighbor objects of the exact matching pairs, and the spatial positions of the BIM objects are adaptively updated.

2. The complex scene three-dimensional object matching method based on first-order distance-topological compatibility index as claimed in claim 1, characterized in that: According to the relative distance and spatial topological positioning features between three-dimensional objects, the first-order distance-topological compatibility index of three-dimensional objects is constructed, including: Calculate the geometric distance between any two three-dimensional objects and obtain the distance matrix; Traverse each row of the distance matrix, and express the relationship between the corresponding 3D object and the other 3D objects in the scene in turn with the key-value pair of "type-distance", so as to complete the generation of the first-order geometric distance positioning feature vector of the 3D object in the global space; The topological relationship of the three-dimensional objects in the scene is established based on the three-dimensional object Voronoi diagram, and the type labels of the neighboring objects of the three-dimensional objects are counted to generate a type label histogram, and the first-order topological feature vector is obtained as the first-order topological positioning feature expression; The first-order geometric distance positioning feature vector and the first-order topological feature vector are used as the first-order distance-topological compatibility index of three-dimensional objects.

3. The complex scene three-dimensional object matching method based on first-order distance-topological compatibility index as claimed in claim 2, characterized in that: The calculation method of the first-order geometric distance positioning feature vector is: Among them, i and j represent two three-dimensional objects, D i→j Represents the geometric distance from 3D object i to j in the scene, C M-1 Indicates the device type of the M-1th 3D object, where M indicates the number of 3D objects in the scene. Represents the first-order distance feature vector of three-dimensional object i; The first-order topological eigenvector is calculated as: Among them, U i represents the first-order topological neighbor set of three-dimensional object i, q represents the first-order topological neighbor of three-dimensional object i, C q represents the type of the first-order topological neighbors of a three-dimensional object i, Indicates that the neighbor type of the three-dimensional object i is C j The number of represents the first-order topological feature vector of three-dimensional object i, and k represents a three-dimensional object.

4. The complex scene three-dimensional object matching method based on first-order distance-topological compatibility index as claimed in claim 1, characterized in that: Based on the first-order distance-topological compatibility index of the real-world point cloud object and the BIM object, the first-order distance similarity between the BIM object and the real-world point cloud object is calculated, including: For the real-time point cloud object s, use its equipment type as a filter tag, obtain BIM objects with the same equipment type, and add them to the matching object set; For the current real-life scenic spot cloud object s and BIM object t, the minimum distance difference is calculated dimension by dimension. Specifically, each key-value pair in the first-order distance feature vector of the real-life scenic spot cloud object s is selected in turn, and the key-value pair set with the same device type in the first-order distance feature vector of the BIM object t is filtered by the device type of the key-value pair. The difference between the distance in the current key-value pair and each distance in the filtered key-value pair set is calculated respectively, and the minimum value of the distance difference is taken as the distance between the real-life scenic spot cloud object s and the BIM object t in the current dimension. Sum the minimum distance differences between the real-world point cloud object s and the BIM object t in all dimensions to obtain the similarity between them in the first-order distance feature, which is used as the first-order distance similarity between the BIM object and the real-world point cloud object; For the real-world point cloud object s and the BIM object t, the spatial topological similarity between the BIM object and the real-world point cloud object is obtained by calculating the quantitative differences in the corresponding dimensions of the first-order topological feature vectors of the two objects and summing up the differences in all dimensions.

5. The complex scene three-dimensional object matching method based on first-order distance-topological compatibility index as claimed in claim 4, characterized in that: The calculation formula of the first-order distance similarity between BIM objects and real-world point cloud objects is: in, It represents the distance difference between the mth dimension of the real-time point cloud object s and the nth dimension of the BIM object t after filtering the same type of key-value pairs, D s→m Indicates the distance value on the mth dimension in the real-time point cloud object s, D t→n Represents the distance value in the nth dimension of BIM object t, represents the minimum distance difference between the real-world point cloud object s and the BIM object t in the mth dimension, Δd s→t It represents the first-order distance similarity between the real-world point cloud object s and the BIM object t, and represents the similarity between the two objects in the first-order geometric distance feature; The calculation formula of the spatial topological similarity between BIM objects and real-world point cloud objects is: and The topological neighbors of the real-time point cloud object s and the BIM object t belong to type C q The number of neighbors of Indicates that in type C q The difference in the number of topological neighbors between the two objects, Δn s→t Represents the spatial topological similarity between the real-world point cloud object s and the BIM object t.

6. The complex scene three-dimensional object matching method based on first-order distance-topological compatibility index according to claim 1, characterized in that: The precise matching pairs are established by maximizing the first-order topological neighbor similarity, including: By maximizing the first-order topological neighbor similarity, the pairs with the largest first-order topological neighbor similarity and the smallest first-order geometric distance are selected from the initial matching pairs as the exact matching pairs.

7. The complex scene three-dimensional object matching method based on first-order distance-topological compatibility index according to claim 1, characterized in that: Search potential matching pairs based on the first-order neighbor objects of the exact matching pairs, and adaptively update the spatial position of the BIM objects, including: Select potential matching pairs from the first-order neighbors of the exact matching pairs, requiring that the neighbors of the potential matching pairs contain exact matching objects and are exact matching pairs of each other; Taking the distance relationship between all accurate matching pairs and potential matching pairs as the constraint condition, a multi-line of sight constraint equations is established to achieve adaptive correction of the spatial position of BIM objects in potential matching pairs.

8. A complex scene three-dimensional object matching device based on first-order distance-topological compatibility index, characterized in that: include: An index construction module is used to construct a first-order distance-topological compatibility index of three-dimensional objects based on the relative distance between the three-dimensional objects and the spatial topological positioning features; An index calculation module is used to calculate the first-order distance-topological compatibility index of the real-world point cloud object and the BIM object respectively according to the spatial layout of the complex three-dimensional object; A similarity calculation module is used to calculate the first-order distance similarity and spatial topological similarity between the BIM object and the real-world point cloud object based on the first-order distance-topological compatibility index of the real-world point cloud object and the BIM object; An initial matching pair generation module is used to generate an initial matching pair by maximizing the first-order distance similarity; An accurate matching pair establishment module is used to establish accurate matching pairs by maximizing the first-order topological neighbor similarity; The adaptive correction module is used to search for potential matching pairs based on the first-order neighbor objects of the exact matching pairs and adaptively update the spatial position of the BIM objects.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for matching three-dimensional objects in complex scenes based on a first-order distance-topological compatibility index as described in any one of claims 1 to 7 is implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the complex scene three-dimensional object matching method based on the first-order distance-topology compatibility index as described in any one of claims 1 to 7 is implemented.

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