CN114913300A - Automatic modeling method for entity three-dimensional model - Google Patents
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Abstract
The invention belongs to the technical field of three-dimensional modeling, and particularly relates to an automatic modeling method of an entity three-dimensional model, which comprises three parts of high-level parameterization of components, low-level parameterization of the components and rapid modeling guided by a rule base.
Description
Technical Field
The invention belongs to the technical field of three-dimensional modeling, and particularly relates to an automatic modeling method for a three-dimensional solid model.
Background
The research of the building-related rapid modeling method gradually becomes a research hotspot in the fields of computer graphics and virtual reality along with the rapid development of technologies such as tourism, animation, games, virtual roaming and the like, particularly in the fields of digital heritage protection, historical culture popularization and the like of ancient buildings, building modeling has practical significance, buildings in different regions have strong building styles, and no matter the traditional CAD software or modeling software is used for building modeling, professional building field knowledge is required, the problems of high cost, low efficiency and the like exist, and the method is not suitable for large-scale rapid modeling. Some rely too much on the user to pre-process them or require a lot of interaction.
In the prior art, the building modeling speed in different areas is low, and the modeling precision is low;
the prior art needs professional knowledge in the building field, and has high cost and low efficiency.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an automatic modeling method of an entity three-dimensional model, which is used for solving the problems of low building modeling speed, low modeling precision, need of professional building field knowledge, high cost, low efficiency and the like in different areas in the prior art.
In order to solve the technical problems, the invention adopts the following technical scheme:
an automatic modeling method of an entity three-dimensional model comprises three parts of high-level parameterization of components, low-level parameterization of the components and rapid modeling guided by a rule base;
the high-level parameterization of the components comprises the following specific steps:
a1, extracting a small amount of high-level features of different types of scene models from the domain rules and the scene model data;
a2, forming a high-level parameter rule base based on a probability network after parameterization, and using the high-level parameter rule base as an interface for quick modeling input of a common user;
the low-level parameterization of the component comprises the following specific steps:
b1, extracting different low-level attributes from the domain rules and the mapping data according to the physical and geometric attribute differences of each functional component, and classifying for parameterization;
b2, designing constraint rules among all attributes to form a low-level parameter rule base;
the specific steps of the rule base guide modeling are as follows:
c1, for the application scene of fast random modeling, realizing the automatic fast modeling task without any manual intervention;
c2, general users without field experience adopt component-by-component guide type guidance modeling, recommend the optimal high-level parameters to the users in real time, and automatically generate the low-level parameters of all scene model modules from the high-level parameters input by the users;
c3, advanced users complete complex modeling of scene models by modifying the building templates or constraint rules.
Further, the component high-level parameterization is used for describing the intuitive and easily understood high-level feature attributes of the scene model, in order to simplify interaction, the high-level parameters are usually designed into discrete values, and for any type of component i, the high-level parameter set is defined as:
H i ＝{H 1 ，H 1 ，...，H k }；
for any type of scene model type s, a small number of special high-level parameters are defined for describing the overall style of the scene model, which are called as top-level parameters of the type of scene model and are expressed as follows:
after the scene model is decomposed into the single components, the components are not isolated but have strong dependency relationship, and in order to realize automation of scene model building, the dependency relationship between the components and the scene model building sequence need to be defined.
Furthermore, the probability relation between the high-level parameterizations of the components naturally forms a probability network structure, parameter network learning is introduced to describe the relation, all selectable states of the high-level parameters of the subsequent required components are deduced in real time according to the high-level parameters of the current components selected by a user in the modeling process, the states with the maximum probability are recommended to the user as default values according to probability value sequencing, the user is allowed to select other selectable states, the user is allowed not to input any parameters, and all high-level parameters are randomly generated by adopting a Markov chain Monte Carlo sampling method.
In order to simplify the interaction, the high-level parameters are usually designed into discrete values, and a simple and clear user interface design is adopted, so that an interface for modifying the style and the style of the scene model is provided for a user.
Further, the parameter network learning is divided into structure learning and parameter learning, and for a scene model type with N high-level parameters, the scene model type can be regarded as a parameter network model with N variables, where N is (G, θ), G represents a model structure, θ represents a model parameter, and X is a variable i In common r i Value, its parent Pa (X) i ) The number of the combinations is q i Then its network can be represented as:
further, the component low-level parameterization defines, for any type of component i, a selectable set of low-level parameters Li ═ { LiT, LiL, LiS, LiP } for describing basic properties of this type of component.
S1, a type attribute LIT is a discrete type parameter and is used for classifying types of different styles, styles and material attributes of the same component, the geometric characteristics of the components of different types are completely different, and a non-discrete type parameter needs to be classified by considering a clustering method or a manual calibration mode to describe the division of the type parameters (large, medium and small) of the house area scale;
s2, a shape attribute LiS, which is used for describing the size and shape of the component and depends on the geometrical characteristics of the type of the component, and for the same component, when the type attribute is a cube, the shape attribute is the length, the width and the height; when the type of the curve is a curved surface type, curve parameter control points or curvatures and the like of the sampling strip are typically square column foundations and curved surface column foundations;
s3, the layout attribute LiL is used to describe layout-related parameters such as the number, the relative position, and the like that need to be satisfied when a plurality of such components are constructed simultaneously, and satisfy the requirement that a plurality of instances of the same component must be constructed at one time.
S4, the position attribute LiP describes the spatial position parameters, such as coordinates and rotation amount, of the component relative to its dependent component placement.
Compared with the prior art, the invention has the following beneficial effects:
1. the method comprises the steps that a parameter network obtained through data learning is used as a high-level parameter rule base of a scene model type, and due to the regionality of the scene model, the selected part of data is taken as a unit, so that the style of the scene model constructed through the high-level parameter rule base can better accord with the characteristic and the appearance of the part;
2. the finally built model can be converted into a grid model for conventional display and rendering, can be compatible with professional scene model field CAD software, and can be used for further analysis and application by combining stored scene model parameter information;
3. the method uses the recommendation system and the rule base to complete the rapid automatic construction of the model, and the automatic construction systems of buildings in different areas realized on the basis have real construction flows and rich internal details of the buildings, the interaction interfaces are simple and clear, users without any domain knowledge can rapidly construct building models with high precision through simple interaction, and the method is suitable for the fields of multi-scene building teaching research, cultural heritage digital protection and the like.
Drawings
FIG. 1 is a schematic diagram of an embodiment of an automatic modeling method for a three-dimensional solid model according to the present invention;
Detailed Description
In order to make the technical solution of the present invention better understood by those skilled in the art, the following technical solution is further explained with reference to the accompanying drawings and examples:
it should be noted that the same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "inner", "outer", etc. are used for indicating the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not indicated or implied that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limiting the patent, and the specific meaning of the terms will be understood by those skilled in the art according to the specific situation.
In addition, the descriptions related to "first", "second", etc. in the present invention are for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature.
In the description of the present invention, unless otherwise explicitly defined or limited, the term "connected" or the like, if appearing to indicate a connection relationship between components, is to be understood broadly, e.g. fixedly connected, also detachably connected, or integrated; is a mechanical connection and is also an electrical connection; are directly connected or indirectly connected through an intermediate member, and are communicated with each other or mutually interacted with each other. The specific meanings of the above terms in the present invention are understood to be specific to those of ordinary skill in the art. In addition, the technical solutions in the embodiments are mutually combined, but must be realized by those skilled in the art, and when the technical solutions are mutually contradictory or cannot be realized, the technical solutions should be considered to be absent and not be within the protection scope of the present invention.
Examples
As shown in FIG. 1, an automatic modeling method for a three-dimensional solid model comprises three parts, namely component high-level parameterization, component low-level parameterization and rule base-guided rapid modeling;
the high-level parameterization of the components comprises the following specific steps:
a1, extracting a small amount of high-level features of different types of scene models from the domain rules and the scene model data;
a2, forming a high-level parameter rule base based on a probability network after parameterization, and using the high-level parameter rule base as an interface for quick modeling input of a common user;
the low-level parameterization of the component comprises the following specific steps:
b1, extracting different low-level attributes from the field rules and the mapping data according to the physical and geometric attribute differences of each functional component, and classifying and parameterizing;
b2, designing constraint rules among all attributes to form a low-level parameter rule base;
the specific steps of the rule base guide modeling are as follows:
c1, for the rapid stochastic modeling application scene, realizing an automatic rapid modeling task without any manual intervention;
c2, general users without field experience adopt component-by-component guide type guidance modeling, recommend the optimal high-level parameters to the users in real time, and automatically generate the low-level parameters of all scene model modules from the high-level parameters input by the users;
c3, advanced users complete complex modeling of scene models by modifying the building templates or constraint rules.
The finally built model can be converted into a grid model for conventional display and rendering, can be compatible with professional scene model field CAD software, and can be used for further analysis and application by combining stored scene model parameter information.
Further, the component high-level parameterization is used for describing the intuitive and easily understood high-level feature attributes of the scene model, in order to simplify interaction, the high-level parameters are usually designed into discrete values, and for any type of component i, the high-level parameter set is defined as:
H i ＝{H 1 ，H 1 ，...，H k }；
for any type of scene model s, a small number of special high-level parameters are defined for describing the overall style of the scene model, which are called as top-level parameters of the type of scene model, and are expressed as follows:
after the scene model is decomposed into the single components, the components are not isolated but have strong dependency relationship, and in order to realize the automation of the construction of the scene model, the dependency relationship between the components and the construction sequence of the scene model need to be defined.
Furthermore, the probability relation between the high-level parameterizations of the components naturally forms a probability network structure, parameter network learning is introduced to describe the relation, all selectable states of the high-level parameters of the subsequent required components are deduced in real time according to the high-level parameters of the current components selected by a user in the modeling process, the states with the maximum probability are recommended to the user as default values according to probability value sequencing, the user is allowed to select other selectable states, the user is allowed not to input any parameters, and all high-level parameters are randomly generated by adopting a Markov chain Monte Carlo sampling method.
In order to simplify the interaction, the high-level parameters are usually designed into discrete values, and a simple and clear user interface design is adopted, so that an interface for modifying the style and the style of the scene model is provided for a user.
Further, the parameter network learning is divided into structure learning and parameter learning, and for a scene model type with N high-level parameters, the scene model type can be regarded as a parameter network model with N variables, where N is (G, θ), G represents a model structure, θ represents a model parameter, and X is a variable i In total of r i Value, its parent Pa (X) i ) The number of the combinations is q i Then its network can be represented as:
of maximum likelihood estimationThe objective is to find θ, so that the probability distribution table for each node of the bayesian network has Maximum Likelihood Estimates (MLEs) for the training data 1 ，D 2 ，...，D M Can be considered to be independently equally distributed with respect to N, the log-likelihood function of θ can be calculated as follows:
θ (K ≈ J) is maximum θ (J | J) θ (J):
at this time, the process of the present invention,
is theta ijk The maximum likelihood estimation is implemented as a classical counting algorithm (counting), data which is not preprocessed may have a phenomenon of missing data, and at this time, a solution may be performed by using a conjugate gradient descent method (conjugate gradient component) or a maximization expectation algorithm (EM for short), and the like.
The method comprises the steps of obtaining a scene model by using a high-level parameter rule base, and establishing a scene model by using a high-level parameter rule base.
Further, the component low-level parameterization can define low-level parameters of any type of component iSelection set L i ＝{L i T ，L i L ，L i S ，L i Each type of low-level property is specified as follows:
s1, type attribute L i T The discrete type parameters are used for classifying types of different styles, styles and material attributes of the same component, the geometric characteristics of the components of different types are completely different, and the non-discrete type parameters need to be classified by considering a clustering method or a manual calibration mode to describe the division of the type parameters (large, medium and small) of the house area scale;
s2, shape attribute L i S, describing the size and shape of the component, wherein the size and shape of the component depend on the geometric characteristics of the type of the component, and for the same component, when the type attribute is a cube, the shape attribute is length, width and height; when the type of the curve is a curved surface type, curve parameter control points or curvatures and the like of the sampling strip are typically square column foundations and curved surface column foundations;
s3, layout Properties
The method is used for describing the number, relative position and other layout related parameters required to be met when a plurality of components are built simultaneously, and meets the requirement that a plurality of instances of the same component must be built at one time.
S4, location Attribute
Spatial position parameters, such as coordinates and rotation amounts, are described with respect to which the components depend on their placement.
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the prior art in this field has the ability to utilize the present invention in any conventional manner before this date. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent.
Claims (5)
1. An automatic modeling method of a solid three-dimensional model is characterized in that: the method comprises three parts of high-level parameterization of components, low-level parameterization of the components and rapid modeling guided by a rule base;
the high-level parameterization of the components comprises the following specific steps:
a1, extracting a small amount of high-rise features of different types of buildings from the domain rules and the building data;
a2, forming a high-level parameter rule base based on a probability network after parameterization, and using the high-level parameter rule base as an interface for quick modeling input of a common user;
the low-level parameterization of the component comprises the following specific steps:
b1, extracting different low-level attributes from the domain rules and the mapping data according to the physical and geometric attribute differences of each functional component, and classifying for parameterization;
b2, designing constraint rules among all attributes to form a low-level parameter rule base;
the specific steps of the rule base guide modeling are as follows:
c1, for the application scene of fast random modeling, realizing the automatic fast modeling task without any manual intervention;
c2, general users without field experience adopt component-by-component guide type guidance modeling, recommend the optimal high-rise parameters to the users in real time, and automatically generate the low-rise parameters of all building modules according to the high-rise parameters input by the users;
c3, advanced users complete complex building modeling by modifying the building templates or constraint rules.
2. The method according to claim 1, wherein the method comprises the following steps: the component high-level parameterization is used for describing intuitive and easily understood high-level characteristic attributes of the building, and the high-level parameters are designed into discrete values for simplifying interaction.
3. The method of claim 2, wherein the method comprises the following steps: the probability relation between the high-level parameterizations of the components naturally forms a probability network structure, parameter network learning is introduced to describe the relation, all selectable states of the high-level parameters of the subsequent required components are deduced in real time according to the high-level parameters of the current components selected by a user in the modeling process, the states with the maximum probability are recommended to the user as default values according to probability value sequencing, the user is allowed to select other selectable states, the user is allowed to not input any parameters, and all the high-level parameters are randomly generated by adopting a Markov chain Monte Carlo sampling method.
4. The method according to claim 3, wherein the method comprises the following steps: the parameter network learning is divided into structure learning and parameter learning, and for a building type with N high-rise parameters, a parameter network model with N variables, namely (G, theta), is considered, wherein G represents a model structure, theta represents a model parameter, and X represents a variable i In common r i Value, its parent Pa (X) i ) The number of the combinations is q i 。
5. The method according to claim 1, wherein the method comprises the following steps: the component low-level parameterization defines a selectable set L of low-level parameters of any type of component i i ＝{L i T ，L i L ，L i s ，L i P for describing the generic part of this type of componentEach type of low-level attribute is specified as follows:
s1, type attribute L i T The discrete type parameters are used for classifying types of different styles, styles and material attributes of the same component, the geometric characteristics of the components of different types are completely different, and the non-discrete type parameters need to be classified by considering a clustering method or a manual calibration mode to describe the division of the type parameters of the house area scale;
s2, shape attribute L i S, describing the size and shape of the component, wherein the size and shape of the component depend on the geometric characteristics of the type of the component, and for the same component, when the type attribute is a cube, the shape attribute is length, width and height; when the type of the curve is a curved surface type, curve parameter control points or curvatures of the sampling bars are typically square column foundations and curved surface column foundations;
s3, layout Properties
The layout-related parameters are used for describing the number and relative positions required to be met when a plurality of components are built simultaneously, and the requirement that a plurality of instances of the same component are built at one time is met.
S4, location Attribute
Spatial position parameters, such as coordinates and rotation amounts, are described with respect to which the components depend on their placement.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115798655A (en) * | 2023-01-25 | 2023-03-14 | 江苏新恒基特种装备股份有限公司 | Method for modeling forge piece through related parameters |
| CN118097065A (en) * | 2024-04-17 | 2024-05-28 | 成都建工第三建筑工程有限公司 | Intelligent building engineering three-dimensional modeling system and method |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106126838A (en) * | 2016-06-28 | 2016-11-16 | 合肥工业大学 | A kind of Huizhou Architecture Science intelligence builds system |
-
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- 2022-03-01 CN CN202210194611.9A patent/CN114913300A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106126838A (en) * | 2016-06-28 | 2016-11-16 | 合肥工业大学 | A kind of Huizhou Architecture Science intelligence builds system |
Non-Patent Citations (2)
| Title |
|---|
| 姚舜才 等: "《机器学习基础教程》", vol. 1, 31 March 2020, 西安电子科学技术大学出版社, pages: 40 - 41 * |
| 李尚林 等: "面向真实构建的徽州建筑快速建模方法", 《软件学报》, no. 6, pages 2542 - 2556 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115798655A (en) * | 2023-01-25 | 2023-03-14 | 江苏新恒基特种装备股份有限公司 | Method for modeling forge piece through related parameters |
| CN118097065A (en) * | 2024-04-17 | 2024-05-28 | 成都建工第三建筑工程有限公司 | Intelligent building engineering three-dimensional modeling system and method |
| CN118097065B (en) * | 2024-04-17 | 2024-07-09 | 成都建工第三建筑工程有限公司 | Intelligent building engineering three-dimensional modeling system and method |
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