A method and system for evaluating the precision depth of SU models

Abstract

The present invention relates to the field of building digitization technology, and specifically to a SU model precision depth evaluation method and system. The evaluation method includes: classifying and marking the model parts of the SU building three-dimensional model according to the component category; determining the number of parts and color richness of the model parts corresponding to each component category, configuring a first precision weight for the number of parts, and configuring a second precision weight for the color richness; determining the visual accuracy of each model part, and configuring a third precision weight for the visual accuracy. Calculate the category accuracy of each component category; configure a fourth precision weight for each component category, calculate the overall accuracy of the SU building three-dimensional model; use the overall accuracy as the basis for evaluating the model precision of the SU building three-dimensional model. It can more reasonably evaluate and compare the model precision of a large-scale building model system, and improve the reference value of the evaluation and comparison results.

Description

Technical Field

[0001] The present invention relates to the field of building digitalization technology, and in particular to a SU model fineness depth evaluation method and system. Background Art

[0002] Sketch Up (abbreviated as SU) is a set of design tools directly oriented to the design scheme creation process. Its creation process can not only fully express the designer's ideas but also fully meet the needs of instant communication with customers. It allows designers to directly carry out very intuitive ideas on the computer and is an excellent tool for the creation of three-dimensional architectural design schemes. SU is widely used in the field of architecture.

[0003] In the prior art, there is usually no strict standard for evaluating the model precision. Some methods are based on empirical judgment by relevant experienced personnel, while others are evaluated through the physical constraints and number of model faces attached to the model file. These methods have obvious limitations.

[0004] In a large-scale building model system, the number of model parts that constitute the large-scale building model system is usually huge, and it is no longer applicable to rely on relevant experienced personnel to make judgments. For a large-scale building model system, the volume of its physical constraints and model faces is very large, and the larger the model system, the smaller the proportion of the difference between the physical constraints and the number of model faces in the total volume, so the reference value of evaluating through the physical constraints and the number of model faces attached to the model file is not high.

[0005] In view of this, this application is hereby filed. Summary of the invention

[0006] The first purpose of the present invention is to provide a SU model fineness depth evaluation method, which can more reasonably evaluate and compare the model fineness of a large-scale building model system, improve the reference value of the evaluation and comparison results, reduce the one-sidedness, and is suitable for objective evaluation and comparison of larger-scale building models.

[0007] The second purpose of the present invention is to provide a SU model fineness depth evaluation system, which can more reasonably evaluate and compare the model fineness of a large-scale building model system, improve the reference value of the evaluation and comparison results, reduce the one-sidedness, and is suitable for objective evaluation and comparison of larger-scale building models.

[0008] The embodiment of the present invention is achieved as follows:

[0009] A method for evaluating the fineness depth of a SU model comprises the following steps:

[0010] S1. Classify and mark the model parts in the SU building three-dimensional model that have been checked and found to be correct according to the component category.

[0011] S2. Determine the number of parts and color richness of the model parts corresponding to each component category, configure a first fineness weight for the number of parts, and configure a second fineness weight for the color richness.

[0012] S3. Determine the visual accuracy of each model part and configure a third precision weight for the visual accuracy.

[0013] S4. Calculate the category accuracy of each component category, category accuracy = Among them, a is the number of parts in the corresponding component category, p1 is the first fineness weight, b is the color richness in the corresponding component category, p2 is the second fineness weight, i=1, 2, 3, ··· , n, ci is the visual accuracy of the i-th model part in the corresponding component category, and p3 is the third precision weight.

[0014] S5. Configure the fourth precision weight for each component category and calculate the overall accuracy of the SU building 3D model. Overall accuracy = . Where x=1, 2, 3, ··· , m, Qx is the category accuracy of the xth component category of the SU building 3D model, and p4 is the fourth precision weight.

[0015] S6. Use the overall accuracy as the basis for evaluating the model refinement of the SU building three-dimensional model.

[0016] Furthermore, in S3, the visual accuracy is obtained through a verification process. The verification process includes the following steps:

[0017] D1. Establish a three-dimensional test space, and construct a spherical hollow shell in the three-dimensional test space. The inner surface of the hollow shell is set as a preset light-absorbing surface, and a preset unidirectional light source is placed at a preset position in the hollow shell. The irradiation direction of the light beam of the preset unidirectional light source is set along a preset direction.

[0018] D2. Extract the model parts that need to determine the visual accuracy as test parts, place the test parts into the hollow shell of the test three-dimensional space, and replace the outer surface of the test parts with the preset mirror.

[0019] D3. Adjust the relative position relationship between the test part and the preset unidirectional light source so that the light beam emitted by the preset unidirectional light source irradiates the surface of the test part at a preset angle, and the distance between the preset unidirectional light source and the irradiation point of the light beam on the test part is equal to the preset distance.

[0020] D4. Move and / or rotate the test part so that the light beam emitted by the preset unidirectional light source traverses the outer surface of the test part. During the movement and / or rotation process, the preset angle and preset distance are kept unchanged.

[0021] D5. Use the ray tracing algorithm to determine the reflection of the light beam emitted by the preset unidirectional light source on the surface of the test part, and collect the light irradiation conditions at various positions on the inner surface of the hollow shell.

[0022] D6, determining the visual accuracy based on the light exposure at various locations on the inner surface of the hollow shell.

[0023] Furthermore, in D6, the more concentrated the positions on the inner surface of the hollow shell that are illuminated are, the lower the visual accuracy of the test part is.

[0024] Furthermore, in D6, the more regular the number of light rays and the light angles at the positions where the inner surface of the hollow shell is irradiated are, the more regular the appearance of the test part is.

[0025] Furthermore, the verification process also includes the following steps:

[0026] D7. Divide the inner surface of the hollow shell into a number of unit areas and assign a unique identifier to each unit area.

[0027] D8. During the execution of D4, the light quantity data and light angle data of each unit area are saved at a preset time interval, and the light quantity data and light angle data saved each time are used as a reference data unit.

[0028] D9. Among all the reference data units, if only the light quantity data or light angle data in the reference data units whose number is less than or equal to the reference threshold value do not have regularity, it indicates that the surface area of ​​the test part corresponding to the light quantity data or light angle data that do not have regularity has modeling defects.

[0029] A SU model fineness depth evaluation system includes: a memory and a processor.

[0030] The memory stores a computer program, and the computer program is configured to execute the above-mentioned SU model fineness depth evaluation method when running.

[0031] The processor is configured to execute the above-mentioned SU model refinement depth evaluation method through a computer program.

[0032] The beneficial effects of the technical solution of the embodiment of the present invention include:

[0033] The SU model precision depth evaluation method provided by the embodiment of the present invention starts from the number of parts of each component category, the color richness and the visual accuracy of each model part, and emphasizes the actual display effect of the building model, that is, the pursuit of the demonstration-level accuracy of the model, which has positive significance for the market display of the model. Therefore, the solution design of this application is more suitable for the precision evaluation of the market demonstration application of the model.

[0034] In general, the SU model fineness depth evaluation method provided in the embodiment of the present invention can more reasonably evaluate and compare the model fineness of a large-scale building model system, improve the reference value of the evaluation and comparison results, reduce the one-sidedness, and is suitable for objective evaluation and comparison of larger-scale building models.

[0035] The SU model fineness depth evaluation system provided by the embodiment of the present invention can more reasonably evaluate and compare the model fineness of a large-scale building model system, improve the reference value of the evaluation and comparison results, reduce the one-sidedness, and is suitable for objective evaluation and comparison of larger-scale building models. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 Schematic diagram of the internal arrangement of the hollow shell (when the test part is spherical);

[0038] Figure 2 A schematic diagram of the internal arrangement of the hollow shell (when the test part is in block shape);

[0039] Figure 3 For the general Figure 2 Schematic diagram of the test part after rotation.

[0040] Description of reference numerals:

[0041] Hollow shell 100; preset light absorption surface 110; preset unidirectional light source 200; test part 300; irradiation point 310. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0043] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0044] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0045] The terms “first”, “second”, “third”, “fourth”, etc. are merely used for distinguishing descriptions and should not be understood as indicating or implying relative importance.

[0046] As shown in this specification and claims, unless the context clearly provides an example, the words "a", "an", "the", etc. do not refer to the singular, but also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0047] The flowcharts used in this specification are used to illustrate the operations performed by the system according to the embodiments of this specification. It is understood that the operations of each step are not necessarily performed precisely in order. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0048] In order to overcome the shortcomings of the prior art, this embodiment provides a SU model fineness depth evaluation method, the method comprising the following steps:

[0049] S1. Classify and mark the model parts in the SU building three-dimensional model that have been checked correctly according to component categories. Component categories can be divided according to actual needs, and this application does not make specific restrictions. For example, component categories may include but are not limited to: beams, columns, walls, doors, windows, flower beds, pools, green plants, trash cans, street lights, cables, etc., and are not limited to these.

[0050] S2. Determine the number of parts and color richness of the model parts corresponding to each component category, configure a first fineness weight for the number of parts, and configure a second fineness weight for the color richness. The color richness can be determined based on the color rendering of the model parts. Among them, the first fineness weight and the second fineness weight can be flexibly set and adjusted according to actual needs, and the evaluation criteria for color richness can also be adjusted according to actual needs, and this application does not make specific restrictions.

[0051] S3. Determine the visual accuracy of each model part. The visual accuracy is related to the fineness of the appearance of the model part. The finer the appearance of the model part, the higher the visual accuracy of the model part. Conversely, the lower the visual accuracy of the model part. Configure the third fineness weight for the visual accuracy. Both the confirmation standard of the visual accuracy and the third fineness weight can be flexibly set and adjusted according to actual needs.

[0052] S4. Calculate the category accuracy of each component category, category accuracy = Among them, a is the number of parts in the corresponding component category, p1 is the first fineness weight, b is the color richness in the corresponding component category, p2 is the second fineness weight, i=1, 2, 3, ··· , n, ci is the visual accuracy of the i-th model part in the corresponding component category, and p3 is the third precision weight.

[0053] S5. Configure the fourth precision weight for each component category and calculate the overall accuracy of the SU building 3D model. Overall accuracy = . Where x=1, 2, 3, ··· , m, Qx is the category accuracy of the xth component category of the SU building 3D model, and p4 is the fourth precision weight.

[0054] S6. Use the overall accuracy as the basis for evaluating the model refinement of the SU building three-dimensional model.

[0055] The inventors of the present application have discovered that most existing model precision evaluation methods start from the modeling process and use the number of feature parameters and the number of constraints in the model to measure the precision of the model, which is actually not directly related to the actual performance of the model.

[0056] For architectural models, especially large-scale ones, the number of characteristic parameters and constraints is not low. Using traditional methods to evaluate them can easily lead to the evaluation results of large-scale architectural models being too precise.

[0057] In fact, the refinement of an architectural model is largely reflected in the visual experience or visual perception it gives people. In other words, the level of refinement of an architectural model is ultimately reflected in the appearance of the model. The number of feature parameters and the number of constraints traditionally used are not directly related to the overall appearance of the architectural model. The number of feature parameters and the number of constraints more reflect the tediousness of the modeling work of the modelers.

[0058] Through the design of the scheme of this application, starting from the number of parts of each component category, the richness of colors, and the visual accuracy of each model part, the actual display effect of the architectural model is emphasized, that is, the demonstration-level accuracy of the model is pursued, which has positive significance for the market display of the model. Therefore, the design of the scheme of this application is more suitable for the fineness evaluation of the market demonstration application of the model.

[0059] In general, the SU model precision depth evaluation method provided in this embodiment can more reasonably evaluate and compare the model precision of a large-scale building model system, improve the reference value of the evaluation and comparison results, reduce the one-sidedness, and is suitable for objective evaluation and comparison of larger-scale building models.

[0060] Optionally, in S3, the visual accuracy of the model parts is obtained through the verification process. Figure 1 , the verification process includes the following steps:

[0061] D1. Establish a three-dimensional test space, and construct a spherical hollow shell 100 in the three-dimensional test space. The inner surface of the hollow shell 100 is set as a preset light absorption surface 110, and a preset unidirectional light source 200 is placed at a preset position in the hollow shell 100. The irradiation direction of the light beam of the preset unidirectional light source 200 is set along a preset direction, and the position and light beam irradiation direction of the preset unidirectional light source 200 are always kept unchanged.

[0062] D2. Extract the model part whose visual accuracy needs to be determined as the test part 300, place the test part 300 into the hollow shell 100 of the test three-dimensional space, and replace the outer surface of the test part 300 with a preset mirror surface.

[0063] D3. Adjust the relative position relationship between the test part 300 and the preset unidirectional light source 200 so that the light beam emitted by the preset unidirectional light source 200 irradiates the surface of the test part 300 at a preset angle, and the distance between the preset unidirectional light source 200 and the irradiation point 310 of the light beam on the test part 300 is equal to the preset distance.

[0064] D4. Move and / or rotate the test part 300 so that the light beam emitted by the preset unidirectional light source 200 traverses the outer surface of the test part 300. In the process of moving and / or rotating the test part 300, the preset angle and the preset distance are kept unchanged, that is, the test is always performed in the state that "the light beam emitted by the preset unidirectional light source 200 irradiates the surface of the test part 300 at the preset angle, and the distance between the preset unidirectional light source 200 and the irradiation point 310 of the light beam on the test part 300 is equal to the preset distance".

[0065] Please refer to Figure 1 When the test part 300 is spherical, in the process of executing D4, it is only necessary to rotate the test part 300 with the center of the sphere as the rotation center to complete the traversal operation. When the test part 300 is not spherical, for example, it is a block, such as Figure 2 As shown, since the outer surface of the test part 300 is composed of multiple plane walls, during the execution of D4, for the outer surface R1 currently being irradiated, the test part 300 needs to be translated along the outer surface R1 currently being irradiated in order to traverse the outer surface R1. After the outer surface R1 is traversed, if the outer surface R2 is to be traversed next, the test part 300 needs to be rotated so that the outer surface R2 is rotated to a state coplanar with the previous outer surface R1, as shown in FIG. Figure 3 As shown, the test part 300 is then translated along the outer surface R2 to complete the traversal of the outer surface R2. Repeating the above operation on each surface of the test part 300 can complete the traversal of all its outer surfaces.

[0066] D5. Use a ray tracing algorithm to determine the reflection condition of the light beam emitted by the preset unidirectional light source 200 on the surface of the test part 300 , and collect the light irradiation conditions at various positions on the inner surface of the hollow shell 100 .

[0067] D6, determining the visual accuracy according to the light irradiation conditions at various positions on the inner surface of the hollow shell 100.

[0068] Specifically, in D6, for the process of executing D4, the more concentrated the positions on the inner surface of the hollow shell 100 where the light is reflected from the outer surface of the test part 300, in other words, the more concentrated the positions on the inner surface of the hollow shell 100 where the light is irradiated, the simpler the morphological design of the outer surface of the test part 300, and the lower the visual accuracy of the test part 300.

[0069] Among them, the correspondence between the distribution concentration degree of the irradiated positions on the inner surface of the hollow shell 100 and the morphological uniformity (or morphological complexity) of the outer surface of the test part 300 can be flexibly adjusted according to actual needs. In other words, the calculation standard for calculating the morphological uniformity (or morphological complexity) of the outer surface of the test part 300 by the distribution concentration degree of the irradiated positions on the inner surface of the hollow shell 100 can be flexibly adjusted according to actual conditions, and the present application does not impose any specific restrictions.

[0070] Optionally, in D6, if during the execution of D4, the number of light rays and the light angle at the position where the inner surface of the hollow shell 100 is irradiated change more regularly with time, the appearance of the test part 300 is more regular. In this way, auxiliary judgment can be made on the regularity of the appearance of the model part.

[0071] Furthermore, the verification process also includes the following steps:

[0072] D7. Divide the inner surface of the hollow shell 100 into a number of unit areas, and assign a unique identifier to each unit area. The size of the unit area can be flexibly determined according to actual needs. The smaller the area of ​​each unit area, the higher the precision of the analysis will be, and the specific accuracy requirements can be flexibly selected according to actual needs.

[0073] D8. During the execution of D4, the light quantity data and the light angle data irradiated to each unit area at the current time are saved at preset time intervals, and the light quantity data and light angle data saved each time are used as a reference data unit. After the outer surface of the test part 300 is traversed, a series of reference data units separated by preset time intervals can be collected. The preset time interval can be flexibly set according to actual needs. The shorter the preset time interval, the higher the precision of the analysis. The specific accuracy requirements can be flexibly selected according to actual needs.

[0074] D9. Among all the reference data units, if only the light quantity data or light angle data in the reference data units that are less than or equal to the reference threshold (the specific value of the reference threshold can be flexibly set according to actual needs) do not have regularity, in other words, the light quantity data or light angle data in the remaining reference data units all have regularity, and the regularity includes but is not limited to periodic changes, this means that the appearance morphology of the test part 300 is basically a regular design, and the positions corresponding to these reference data units that do not have regularity may have model defects, then it is indicated that the surface area of ​​the test part 300 corresponding to the light quantity data or light angle data that do not have regularity has modeling defects, that is, the surface area of ​​the test part 300 corresponding to the reference data units that do not have regularity has modeling defects.

[0075] This design can help to supplement the modeling defects in the SU building three-dimensional model that were missed in S1, and help further eliminate the defects and errors that may exist in the SU building three-dimensional model.

[0076] This embodiment also provides a SU model fineness depth evaluation system, which includes: a memory and a processor.

[0077] The memory stores a computer program, and the computer program is configured to execute the above-mentioned SU model fineness depth evaluation method when running.

[0078] The processor is configured to execute the above-mentioned SU model refinement depth evaluation method through a computer program.

[0079] In summary, the SU model fineness depth evaluation method provided in the embodiment of the present invention can more reasonably evaluate and compare the model fineness of a large-scale building model system, improve the reference value of the evaluation and comparison results, reduce the one-sidedness, and is suitable for objective evaluation and comparison of larger-scale building models.

[0080] The SU model fineness depth evaluation system provided by the embodiment of the present invention can more reasonably evaluate and compare the model fineness of a large-scale building model system, improve the reference value of the evaluation and comparison results, reduce the one-sidedness, and is suitable for objective evaluation and comparison of larger-scale building models.

[0081] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A SU model fineness depth evaluation method, characterized in that: The steps include: S1. Classify and mark the model parts in the SU building three-dimensional model that have been checked and found to be correct according to the component categories; S2, determining the number of parts and color richness of the model parts corresponding to each component category, configuring a first fineness weight for the number of parts, and configuring a second fineness weight for the color richness; S3, determining the visual accuracy of each of the model parts, and configuring a third precision weight for the visual accuracy; S4. Calculate the category accuracy of each component category, where category accuracy = ; Wherein, a is the number of parts in the corresponding component category, p1 is the first fineness weight, b is the color richness in the corresponding component category, p2 is the second fineness weight, i=1, 2, 3, ··· , n, ci is the visual accuracy of the i-th model part in the corresponding component category, and p3 is the third precision weight; S5. Assign a fourth precision weight to each component category, and calculate the overall accuracy of the SU building three-dimensional model, wherein the overall accuracy = ; where x=1, 2, 3, ··· , m, Qx is the category accuracy of the xth component category of the SU building three-dimensional model, and p4 is the fourth precision weight; S6. Using the overall accuracy as a basis for evaluating the model refinement of the SU building three-dimensional model.

2. The SU model fineness depth evaluation method according to claim 1 is characterized in that: In S3, the visual accuracy is obtained through a verification process; the verification process includes the following steps: D1. Establish a three-dimensional test space, and construct a spherical hollow shell in the three-dimensional test space, wherein the inner surface of the hollow shell is set as a preset light absorption surface, and a preset unidirectional light source is placed at a preset position in the hollow shell, and the irradiation direction of the light beam of the preset unidirectional light source is set along a preset direction; D2. Extract the model part for which the visual accuracy needs to be determined as a test part, place the test part into the hollow shell of the test three-dimensional space, and replace the outer surface of the test part with a preset mirror surface; D3, adjusting the relative positional relationship between the test part and the preset unidirectional light source, so that the light beam emitted by the preset unidirectional light source irradiates the surface of the test part at a preset angle, and the distance between the preset unidirectional light source and the irradiation point of the light beam on the test part is equal to the preset distance; D4. Move and / or rotate the test part so that the light beam emitted by the preset unidirectional light source traverses the outer surface of the test part; during the movement and / or rotation process, keep the preset angle and the preset distance unchanged; D5. Using a ray tracing algorithm to determine the reflection of the light beam emitted by the preset unidirectional light source on the surface of the test part, and collecting the light irradiation conditions at various positions on the inner surface of the hollow shell; D6, determining the visual accuracy according to light exposure conditions at various positions on the inner surface of the hollow shell.

3. The SU model fineness depth evaluation method according to claim 2 is characterized in that: In D6, the more concentrated the positions where the inner surface of the hollow shell is illuminated are, the lower the visual accuracy of the test part is.

4. The SU model fineness depth evaluation method according to claim 2 is characterized in that: In D6, the more regular the number of light rays and the light angles at the positions where the inner surface of the hollow shell is irradiated are, the more regular the outer shape of the test part is.

5. The SU model fineness depth evaluation method according to claim 2 is characterized in that: The verification process also includes the following steps: D7. Divide the inner surface of the hollow shell into a plurality of unit areas, and assign a unique identifier to each of the unit areas; D8. During the execution of D4, the light quantity data and the light angle data of each unit area are saved at a preset time interval, and the light quantity data and the light angle data saved each time are used as a reference data unit; D9. Among all the reference data units, if only the light quantity data or the light angle data in the reference data units whose number is less than or equal to the reference threshold value do not have regularity, it is indicated that the surface area of ​​the test part corresponding to the light quantity data or the light angle data that do not have regularity has modeling defects.

6. A SU model fineness depth evaluation system, characterized in that: include: Memory and processor; The memory stores a computer program, and the computer program is configured to execute the SU model fineness depth evaluation method according to any one of claims 1 to 5 when running; The processor is configured to execute the SU model fineness depth evaluation method as described in any one of claims 1 to 5 through the computer program.

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