CN113570725A - Three-dimensional surface reconstruction method and device based on clustering, server and storage medium - Google Patents
Three-dimensional surface reconstruction method and device based on clustering, server and storage medium Download PDFInfo
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Abstract
The application is applicable to the technical field of visual image processing, and provides a three-dimensional surface reconstruction method based on clustering, a device, a server and a storage medium, wherein the method comprises the following steps: acquiring point clouds to be processed; clustering and dividing the point cloud to be processed, and forming a rough three-dimensional model according to the point cloud to be processed after clustering and dividing; and extracting the surface area covered by the surface of the rough three-dimensional model as a surface triangle, and taking the rough three-dimensional model with the surface covered by the surface triangle as a target three-dimensional model. Therefore, the point cloud is processed in a clustering segmentation mode, so that the generated model is more in line with the visual intuition of people, better surface reconstruction effects are obtained on objects such as planes, folding angles and column shapes, and the technical problem that the surface of the generated three-dimensional model is not regular in the prior art is solved.
Description
Technical Field
The application belongs to the technical field of visual image processing, and particularly relates to a three-dimensional surface reconstruction method and device based on clustering, a server and a storage medium.
Background
Three-dimensional reconstruction refers to the establishment of a mathematical model suitable for computer representation and processing of a three-dimensional object, is the basis for processing, operating and analyzing the properties of the three-dimensional object in a computer environment, and is also a key technology for establishing virtual reality expressing an objective world in a computer. Generally, in the prior art, a three-dimensional reconstruction process is to acquire depth data and color data through an image acquisition device to reconstruct a three-dimensional model, but the surface of the generated three-dimensional model is not very regular, so that the generated three-dimensional model does not conform to visual perception of a user.
Disclosure of Invention
The embodiment of the application provides a three-dimensional surface reconstruction method, a three-dimensional surface reconstruction device, a server and a readable storage medium based on clustering, and can solve the problem that the surface of a three-dimensional model generated in the three-dimensional reconstruction process in the prior art is not regular.
In a first aspect, an embodiment of the present application provides a method for reconstructing a three-dimensional surface based on clustering, including:
acquiring point clouds to be processed;
clustering and dividing the point cloud to be processed, and forming a rough three-dimensional model according to the point cloud to be processed after clustering and dividing;
and extracting the surface area covered by the surface of the rough three-dimensional model as a surface triangle, and taking the rough three-dimensional model with the surface covered by the surface triangle as a target three-dimensional model.
In a possible implementation manner of the first aspect, acquiring a point cloud to be processed includes:
acquiring a panoramic image to be processed;
and generating a point cloud to be processed according to the panoramic image to be processed.
In a possible implementation manner of the first aspect, performing cluster segmentation on the point cloud to be processed, and forming a rough three-dimensional model according to the point cloud to be processed after the cluster segmentation, includes:
smoothing the point cloud to be processed to obtain a target point cloud;
determining a feature vector of the target point cloud;
clustering point clouds belonging to the same classification to obtain a point cloud set;
and forming a rough three-dimensional model according to the point cloud set.
In a possible implementation manner of the first aspect, smoothing the point cloud to be processed to obtain a target point cloud includes:
determining non-target point clouds in the point clouds to be processed;
and removing non-target point clouds in the point clouds to be processed according to a preset smoothing algorithm to obtain target point clouds.
In a possible implementation manner of the first aspect, before extracting a surface area covered by the surface of the rough three-dimensional model as a surface triangle and taking the rough three-dimensional model with the surface covered by the surface triangle as a target three-dimensional model, the method further includes:
and carrying out estimation clustering on the surface areas covered by the rough three-dimensional model surface.
In a possible implementation manner of the first aspect, extracting a surface area covered by the surface of the rough three-dimensional model as a surface triangle, and taking the rough three-dimensional model with the surface covered by the surface triangle as a target three-dimensional model includes:
calculating an indicating function of the point cloud set top point according to the distance from the surface area covered by the rough three-dimensional model surface to the clustering center point of the point cloud set;
and performing equivalence processing on the surface area according to the indication function to obtain a surface triangle, and taking a rough three-dimensional model with the surface covered with the surface triangle as a target three-dimensional model.
In a second aspect, an embodiment of the present application provides a three-dimensional surface reconstruction apparatus based on clustering, including:
the acquisition module is used for acquiring point clouds to be processed;
the cluster segmentation module is used for carrying out cluster segmentation on the point cloud to be processed and forming a rough three-dimensional model according to the point cloud to be processed after the cluster segmentation;
and the extraction module is used for extracting the surface area covered by the surface of the rough three-dimensional model into a surface triangle and taking the rough three-dimensional model covered by the surface triangle as a target three-dimensional model.
In one possible implementation manner, the obtaining module includes:
the acquisition submodule is used for acquiring a panoramic image to be processed;
and the generation submodule is used for generating a point cloud to be processed according to the panoramic image to be processed.
In one possible implementation manner, the cluster segmentation module includes:
the smoothing sub-module is used for smoothing the point cloud to be processed to obtain a target point cloud;
a determining submodule for determining a feature vector of the target point cloud;
the clustering submodule is used for clustering point clouds belonging to the same classification to obtain a point cloud set;
and the forming submodule is used for forming a rough three-dimensional model according to the point cloud set.
In one possible implementation manner, the smoothing sub-module includes:
the determining unit is used for determining non-target point clouds in the point clouds to be processed;
and the removing unit is used for removing the non-target point cloud in the point cloud to be processed according to a preset smoothing algorithm to obtain the target point cloud.
In one possible implementation manner, the apparatus further includes:
and the estimated distance module is used for carrying out estimated clustering on the surface areas covered by the surface of the rough three-dimensional model.
In one possible implementation manner, the extraction module includes:
the clustering submodule is used for calculating an indicating function of the point cloud set top point according to the distance from the surface area covered by the rough three-dimensional model surface to the clustering center point of the point cloud set;
and the equivalence processing module is used for performing equivalence processing on the surface area according to the indication function to obtain a surface triangle, and taking the rough three-dimensional model with the surface covered with the surface triangle as a target three-dimensional model.
In a third aspect, an embodiment of the present application provides a server, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements the method according to the first aspect.
In a fourth aspect, the present application provides a storage medium storing a computer program, which when executed by a processor implements the method according to the first aspect.
Compared with the prior art, the embodiment of the application has the advantages that:
in the embodiment of the application, point clouds to be processed are obtained; clustering and dividing the point cloud to be processed, and forming a rough three-dimensional model according to the point cloud to be processed after clustering and dividing; and extracting the surface area covered by the surface of the rough three-dimensional model as a surface triangle, and taking the rough three-dimensional model with the surface covered by the surface triangle as a target three-dimensional model. Therefore, the point cloud is processed in a clustering segmentation mode, so that the generated model is more in line with the visual intuition of people, better surface reconstruction effects are obtained for objects such as planes, folding angles and column shapes, and the technical problem that the surface of the generated three-dimensional model is not regular in the prior art is solved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a three-dimensional clustering-based surface reconstruction method provided in an embodiment of the present application;
fig. 2 is a schematic flowchart of a specific implementation of step S102 in fig. 1 of the clustering-based three-dimensional surface reconstruction method according to an embodiment of the present application;
fig. 3 is a schematic flowchart of an implementation of step S104 in fig. 1 of the clustering-based three-dimensional surface reconstruction method according to the embodiment of the present application;
fig. 4 is a schematic flowchart of a specific implementation of step S302 in fig. 3 of the clustering-based three-dimensional surface reconstruction method according to an embodiment of the present application;
fig. 5 is a schematic diagram illustrating a specific flowchart implementation of step S106 in fig. 1 of the clustering-based three-dimensional surface reconstruction method according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a three-dimensional cluster-based surface reconstruction apparatus provided in an embodiment of the present application;
fig. 7 is a schematic structural diagram of a server according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
The technical solutions provided in the embodiments of the present application will be described below with specific embodiments.
Referring to fig. 1, a flow diagram of a method for reconstructing a three-dimensional surface based on clustering provided in an embodiment of the present application is shown, by way of example and not limitation, the method may be applied to a server connected to a depth camera, where the server includes, but is not limited to, a computing device such as a cloud server, and the method may include the following steps:
and S102, acquiring a point cloud to be processed.
In a specific application, as shown in fig. 2, the acquiring of the point cloud to be processed for the specific implementation process schematic diagram of step S102 in fig. 1 of the clustering-based three-dimensional surface reconstruction method provided in the embodiment of the present application includes:
and step S202, acquiring a panoramic image to be processed.
The global image to be processed is a panoramic image obtained by shooting in any scene through a depth camera. The depth camera of the embodiment of the application can be an eight-eye camera, namely the eight-eye camera is composed of an upper group of fish-eye lenses and a lower group of fish-eye lenses, and the four lenses collect four groups of lens images respectively and are spliced into a 360-degree panorama.
And S204, generating a point cloud to be processed according to the panoramic image to be processed.
In the specific application, feature points of a global image to be processed are extracted according to Harris corner detection, FAST corner detection, SIFI extraction algorithm or SURF extraction algorithm, the feature points are processed according to SFM algorithm, depth information and position information of a depth camera are calculated, and three-dimensional coordinates of point cloud are obtained according to the following formula:
wherein, (u, v) is the pixel coordinate of each target feature point in the panoramic image, d is the depth value of each target feature point in the panoramic image, K is the internal reference of the depth camera, and (X, Y, Z) is the three-dimensional coordinate of the point cloud. Illustratively, the internal parameters of the depth camera may be calculated using the Zhang-friend scaling method.
And S104, performing cluster segmentation on the point cloud to be processed, and forming a rough three-dimensional model according to the point cloud to be processed after the cluster segmentation.
In a specific application, as shown in fig. 3, for a specific implementation process schematic diagram of step S104 in fig. 1 of the clustering-based three-dimensional surface reconstruction method provided in the embodiment of the present application, clustering and segmenting are performed on point clouds to be processed, and a rough three-dimensional model is formed according to the point clouds to be processed after clustering and segmenting, including:
and S302, smoothing the point cloud to be processed to obtain a target point cloud.
Exemplarily, as shown in fig. 4, for a specific implementation flow diagram of step S302 in fig. 3 of the clustering-based three-dimensional surface reconstruction method provided in the embodiment of the present application, smoothing the point cloud to be processed to obtain a target point cloud includes:
and S402, determining non-target point clouds in the point clouds to be processed.
The non-target point cloud refers to the point cloud of the outlier and the unsmooth area in the point cloud to be processed.
And S404, removing non-target point clouds in the point clouds to be processed according to a preset smoothing algorithm to obtain target point clouds.
The budget smoothing algorithm includes, but is not limited to, an averaging method, an exponential moving average method, or a 5G filtering method.
It can be appreciated that the embodiments of the present application remove outliers and point clouds of non-smooth areas to reduce the impact on subsequent processing.
And step S304, determining a feature vector of the target point cloud.
It is understood that the process of determining the feature vector of the target point cloud is to numerically express the relationship features between the point clouds by using the normal vector and the weight.
And S306, clustering the point clouds belonging to the same classification to obtain a point cloud set.
In specific application, coordinates and feature vectors of a target point cloud are input into a Support Vector Machine (SVM) trained in advance, the point cloud is divided into determined classifications, such as a plane, a cylinder, a sphere and a curved surface, and the point clouds of the same classification form a point cloud set.
The pre-trained support vector machine can be obtained by training according to the open source database.
And S308, forming a rough three-dimensional model according to the point cloud set.
And S106, extracting the surface area covered by the surface of the rough three-dimensional model as a surface triangle, and taking the rough three-dimensional model with the surface covered by the surface triangle as a target three-dimensional model.
In an optional embodiment, before extracting the surface area covered by the surface of the rough three-dimensional model as a surface triangle and using the rough three-dimensional model with the surface covered by the surface triangle as the target three-dimensional model, the method further includes:
and performing estimation clustering between surface areas covered by the surfaces of the rough three-dimensional models.
In a specific application, two planes which are perpendicular to each other and are adjacent to each other are combined into an L-shaped cluster, so that the algorithm is more robust under noise.
In a specific application, as shown in fig. 5, for a specific flow implementation schematic diagram of step S106 in fig. 1 of the clustering-based three-dimensional surface reconstruction method provided in the embodiment of the present application, extracting a surface region covered by a surface of a rough three-dimensional model as a surface triangle, and using the rough three-dimensional model with the surface covered by the surface triangle as a target three-dimensional model includes:
and step S502, calculating an indication function of the point cloud set vertex according to the distance from the surface area covered by the rough three-dimensional model surface to the clustering center point of the point cloud set.
And S504, performing equivalence processing on the surface area according to the indication function to obtain a surface triangle, and taking the rough three-dimensional model with the surface covered with the surface triangle as a target three-dimensional model.
In the specific application, an indication function of a point cloud set vertex is calculated according to the distance from a surface area covered by the surface of the rough three-dimensional model to a clustering center point of the point cloud set by adopting a MatchingCube algorithm, then the surface area is subjected to equivalence processing according to the indication function to obtain a surface triangle, and the rough three-dimensional model covered by the surface triangle is used as a target three-dimensional model. The MatchingCube algorithm is a classical algorithm for extracting an isosurface from a three-dimensional discrete data field.
Preferably, after extracting the surface area covered by the surface of the rough three-dimensional model as a surface triangle and using the rough three-dimensional model with the surface covered by the surface triangle as the target three-dimensional model, the method further includes: and performing surface reduction treatment on the surface triangle of the target three-dimensional model, and performing parametric ice melting extraction mapping on the target three-dimensional model subjected to the surface reduction treatment. It can be understood that the model is represented by fewer triangles, so that the data size can be reduced, and the model is convenient to display on a WEB side.
In the embodiment of the application, point clouds to be processed are obtained; clustering and dividing the point cloud to be processed, and forming a rough three-dimensional model according to the point cloud to be processed after clustering and dividing; and extracting the surface area covered by the surface of the rough three-dimensional model as a surface triangle, and taking the rough three-dimensional model with the surface covered by the surface triangle as a target three-dimensional model. Therefore, the point cloud is processed in a clustering segmentation mode, so that the generated model is more in line with the visual intuition of people, better surface reconstruction effects are obtained for objects such as planes, folding angles and column shapes, and the technical problem that the surface of the generated three-dimensional model is not regular in the prior art is solved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Corresponding to the method for reconstructing a three-dimensional surface based on clustering described in the above embodiments, fig. 6 shows a structural block diagram of a device for reconstructing a three-dimensional surface based on clustering provided in the embodiments of the present application, and for convenience of description, only the parts related to the embodiments of the present application are shown.
Referring to fig. 6, the apparatus includes:
the acquisition module 61 is used for acquiring point clouds to be processed;
the clustering and partitioning module 62 is used for clustering and partitioning the point cloud to be processed, and forming a rough three-dimensional model according to the point cloud to be processed after clustering and partitioning;
and the extracting module 63 is configured to extract the surface area covered by the surface of the rough three-dimensional model as a surface triangle, and use the rough three-dimensional model with the surface covered by the surface triangle as the target three-dimensional model.
In one possible implementation manner, the obtaining module includes:
the acquisition submodule is used for acquiring a panoramic image to be processed;
and the generation submodule is used for generating a point cloud to be processed according to the panoramic image to be processed.
In one possible implementation manner, the cluster segmentation module includes:
the smoothing sub-module is used for smoothing the point cloud to be processed to obtain a target point cloud;
a determining submodule for determining a feature vector of the target point cloud;
the clustering submodule is used for clustering point clouds belonging to the same classification to obtain a point cloud set;
and the forming submodule is used for forming a rough three-dimensional model according to the point cloud set.
In one possible implementation manner, the smoothing sub-module includes:
the determining unit is used for determining non-target point clouds in the point clouds to be processed;
and the removing unit is used for removing the non-target point cloud in the point cloud to be processed according to a preset smoothing algorithm to obtain the target point cloud.
In one possible implementation manner, the apparatus further includes:
and the estimated distance module is used for carrying out estimated clustering on the surface areas covered by the surface of the rough three-dimensional model.
In one possible implementation manner, the extraction module includes:
the clustering submodule is used for calculating an indicating function of the point cloud set top point according to the distance from the surface area covered by the rough three-dimensional model surface to the clustering center point of the point cloud set;
and the equivalence processing module is used for performing equivalence processing on the surface area according to the indication function to obtain a surface triangle, and taking the rough three-dimensional model with the surface covered with the surface triangle as a target three-dimensional model.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
Fig. 7 is a schematic structural diagram of a server according to an embodiment of the present application. As shown in fig. 7, the server 7 of this embodiment includes: at least one processor 70, a memory 71, and a computer program 72 stored in the memory 71 and executable on the at least one processor 70, the processor 70 implementing the steps in any of the various method embodiments described above when executing the computer program 72.
The server 7 may be a computing device such as a cloud server. The server may include, but is not limited to, a processor 70, a memory 71. Those skilled in the art will appreciate that fig. 7 is merely an example of the server 7, and does not constitute a limitation of the server 7, and may include more or less components than those shown, or combine certain components, or different components, such as input output devices, network access devices, etc.
The Processor 70 may be a Central Processing Unit (CPU), and the Processor 70 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 71 may in some embodiments be an internal storage unit of the server 7, such as a hard disk or a memory of the server 7. The memory 71 may also be an external storage device of the server 7 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the server 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the server 7. The memory 71 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer program. The memory 71 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The embodiment of the present application further provides a storage medium, which is a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the above-mentioned method embodiments.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a server, recording medium, computer Memory, Read-Only Memory (ROM), Random-Access Memory (RAM), electrical carrier wave signals, telecommunications signals, and software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A three-dimensional surface reconstruction method based on clustering is characterized by comprising the following steps:
acquiring point clouds to be processed;
clustering and dividing the point cloud to be processed, and forming a rough three-dimensional model according to the point cloud to be processed after clustering and dividing;
and extracting the surface area covered by the surface of the rough three-dimensional model as a surface triangle, and taking the rough three-dimensional model with the surface covered by the surface triangle as a target three-dimensional model.
2. The method of claim 1, wherein the obtaining of the point cloud to be processed comprises:
acquiring a panoramic image to be processed;
and generating a point cloud to be processed according to the panoramic image to be processed.
3. The method of claim 1, wherein the clustering segmentation is performed on the point cloud to be processed, and a rough three-dimensional model is constructed according to the point cloud to be processed after the clustering segmentation, comprising:
smoothing the point cloud to be processed to obtain a target point cloud;
determining a feature vector of the target point cloud;
clustering point clouds belonging to the same classification to obtain a point cloud set;
and forming a rough three-dimensional model according to the point cloud set.
4. The clustering-based three-dimensional surface reconstruction method of claim 3, wherein smoothing the point cloud to be processed to obtain a target point cloud comprises:
determining non-target point clouds in the point clouds to be processed;
and removing non-target point clouds in the point clouds to be processed according to a preset smoothing algorithm to obtain target point clouds.
5. The method of claim 1, wherein the extracting the surface area covered by the surface of the rough three-dimensional model as a surface triangle, and before the rough three-dimensional model with the surface covered by the surface triangle is used as the target three-dimensional model, the method further comprises:
and carrying out estimation clustering on the surface areas covered by the rough three-dimensional model surface.
6. The clustering-based three-dimensional surface reconstruction method according to claim 1, wherein extracting the surface area covered by the surface of the rough three-dimensional model as a surface triangle, and taking the rough three-dimensional model with the surface covered by the surface triangle as a target three-dimensional model comprises:
calculating an indicating function of the point cloud set top point according to the distance from the surface area covered by the rough three-dimensional model surface to the clustering center point of the point cloud set;
and performing equivalence processing on the surface area according to the indication function to obtain a surface triangle, and taking a rough three-dimensional model with the surface covered with the surface triangle as a target three-dimensional model.
7. A three-dimensional cluster-based surface reconstruction apparatus, comprising:
the acquisition module is used for acquiring point clouds to be processed;
the cluster segmentation module is used for carrying out cluster segmentation on the point cloud to be processed to obtain a point cloud set belonging to the same classification, and a rough three-dimensional model is formed according to the point cloud set;
and the extraction module is used for extracting the surface area covered by the surface of the rough three-dimensional model into a surface triangle and taking the rough three-dimensional model covered by the surface triangle as a target three-dimensional model.
8. The cluster-based three-dimensional surface reconstruction apparatus of claim 7, wherein the obtaining module comprises:
the acquisition submodule is used for acquiring a panoramic image to be processed;
and the generation submodule is used for generating a point cloud to be processed according to the panoramic image to be processed.
9. A server comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 6 when executing the computer program.
10. A storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method according to any one of claims 1 to 6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114882192A (en) * | 2022-07-08 | 2022-08-09 | 浙江国遥地理信息技术有限公司 | Building facade segmentation method and device, electronic equipment and storage medium |
| CN115661426A (en) * | 2022-12-15 | 2023-01-31 | 山东捷瑞数字科技股份有限公司 | Model modification method, device, equipment and medium based on three-dimensional engine |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114882192A (en) * | 2022-07-08 | 2022-08-09 | 浙江国遥地理信息技术有限公司 | Building facade segmentation method and device, electronic equipment and storage medium |
| CN115661426A (en) * | 2022-12-15 | 2023-01-31 | 山东捷瑞数字科技股份有限公司 | Model modification method, device, equipment and medium based on three-dimensional engine |
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