CN109272567A - Model optimization method and apparatus - Google Patents
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- CN109272567A CN109272567A CN201811444028.9A CN201811444028A CN109272567A CN 109272567 A CN109272567 A CN 109272567A CN 201811444028 A CN201811444028 A CN 201811444028A CN 109272567 A CN109272567 A CN 109272567A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2210/00—Indexing scheme for image generation or computer graphics
- G06T2210/04—Architectural design, interior design
Abstract
The embodiment of the present invention provides a kind of model optimization method and apparatus, wherein the model optimization method includes obtaining the hierarchical information of each submodel in model to be processed；Classify according to the whether identical each submodel in the model to be processed of hierarchical information to form multiple file packets to be processed, the hierarchical information for each submodel for including in each file packet to be processed is identical；For each file packet to be processed, each submodel in the file packet to be processed is merged to obtain initial model file based on mesh grid property；Judge whether the mesh mesh parameter in the initial model file meets the first preset need, if not satisfied, optimisation strategy corresponding with the mesh mesh parameter is then called to carry out mesh grid optimization to the initial model file to obtain first object file.The present invention can be realized the mass optimization processing of model, improve model optimization efficiency.
Description
Technical field
The present invention relates to technical field of image processing, in particular to a kind of model optimization method and apparatus.
Background technique
When constructing such as building, landform, trees, river threedimensional model, three-dimensional visualization has been increasingly becoming visually
Main trend.Wherein, the computer performance consumed when rendering in three-dimensional scenic to a large amount of model to reduce, needs
Optimization processing is done to model, currently used model optimization method mainly when creating model, passes through reduction model vertices number
It is realized with modes such as face numbers, or change render engine renders parameter, but model optimization method above-mentioned is based on manual mostly
Mode implementation model is optimized and revised, or using targetedly Optimized code implementation model optimization etc., model optimization is caused to be imitated
Rate is low, and not formed global optimization standard and process, and it is even more impossible to realize to also fail to realize mass and automatic processing.
Summary of the invention
In view of this, the embodiment of the present invention is designed to provide a kind of model optimization method and apparatus, it is above-mentioned to improve
Problem.
On the one hand, present pre-ferred embodiments provide a kind of model optimization method, and the model optimization method includes:
Obtain the hierarchical information of each submodel in model to be processed；
Classify according to the whether identical each submodel in the model to be processed of hierarchical information with formed it is multiple to
File packet is handled, the hierarchical information for each submodel for including in each file packet to be processed is identical；
For each file packet to be processed, based on mesh grid property to each submodel in the file packet to be processed into
Row merges to obtain initial model file；
Judge whether the mesh mesh parameter in the initial model file meets the first preset need, if not satisfied, then
Optimisation strategy corresponding with the mesh mesh parameter is called to carry out mesh grid optimization to the initial model file to obtain
First object file.
Further, the mesh mesh parameter includes number of vertex and face number, is judged in the initial model file
Whether mesh mesh parameter meets the first preset need, if not satisfied, then calling optimization corresponding with the mesh mesh parameter
Strategy carries out the step of mesh grid optimization is to obtain first object file to the initial model file, comprising:
Whether the number of vertex or face number for judging the initial model file are greater than the first preset value, if more than then calling and subtracting
Face strategy carries out subtracting surface treatment to the initial model file, until the number of vertex or the face number are pre- no more than described first
If value, completion is subtracted into the initial model file of surface treatment as first object file.
Further, the model optimization method further include:
Multiple material ball files corresponding to the first object file are merged to obtain material ball file to be processed；
It is merged based on the material ball file to be processed multiple textures files corresponding to each material ball file to obtain
To textures file to be processed；
Judge whether the textures parameter of the textures file to be processed meets the second preset need, if not satisfied, then calling
Optimisation strategy corresponding with the textures parameter optimizes to obtain the second file destination the textures file to be processed.
Further, the textures parameter includes image resolution ratio, judges the textures parameter of the textures file to be processed
Whether second preset need is met, if not satisfied, then calling optimisation strategy corresponding with the textures parameter to described to be processed
Textures file optimizes the step of processing obtains the second file destination, comprising:
Judge whether described image resolution ratio is greater than the second preset value, if more than second preset value, then to it is described to
The image resolution ratio of processing textures file, which optimizes, is adjusted so that the image resolution ratio of the textures file to be processed is little
In second preset value, the first object file of resolution adjustment will be completed as the second file destination.
Further, the method also includes:
The first object file or second file destination are named according to default naming rule.
Further, the hierarchical information of each submodel includes type of service.
On the other hand, present pre-ferred embodiments provide a kind of model optimization device, and the model optimization device includes:
Hierarchical information obtains module, for obtaining the hierarchical information of each submodel in model to be processed；
Hierarchical classification module, for being carried out according to the whether identical each submodel in the model to be processed of hierarchical information
Classification is to form multiple file packets to be processed, the hierarchical information phase for each submodel for including in each file packet to be processed
Together；
First merging module, for being directed to each file packet to be processed, based on mesh grid property to the text to be processed
Each submodel in part packet is merged to obtain initial model file；
First judgment module, for judging it is default whether the mesh mesh parameter in the initial model file meets first
Demand, if not satisfied, then optimisation strategy corresponding with the mesh mesh parameter is called to carry out the initial model file
Mesh grid optimization is to obtain first object file.
Further, the mesh mesh parameter includes number of vertex and face number, and the first judgment module is also used to
Whether the number of vertex or face number for judging the initial model file are greater than the first preset value, if more than then calling and subtracting
Face strategy carries out subtracting surface treatment to the initial model file, until the number of vertex or the face number are pre- no more than described first
If value, completion is subtracted into the initial model file of surface treatment as first object file.
Further, the model optimization device further include:
Second merging module, for merging the corresponding multiple material ball files of the first object file to obtain
Material ball file to be processed；
Third merging module is based on the material ball file to be processed multiple textures files corresponding to each material ball file
It merges to obtain textures file to be processed；
Second judgment module, for judging whether the textures parameter of the textures file to be processed meets the second default need
Ask, if not satisfied, then call corresponding with textures parameter optimisation strategy to the textures file to be processed optimize with
Obtain the second file destination.
Further, the textures parameter includes image resolution ratio, and second judgment module is also used to judge the figure
As whether resolution ratio is greater than the second preset value, if more than second preset value, then to the image of the textures file to be processed
Resolution ratio, which optimizes, is adjusted so that the image resolution ratio of the textures file to be processed is not more than second preset value, will
The first object file of resolution adjustment is completed as the second file destination.
Compared with prior art, the embodiment of the present invention provides a kind of model optimization method and apparatus, wherein the present invention is based on
Unitized, rationalization model optimization standard implementation can effectively remove mould to the mass of model, the optimization processing of automation
Artificial participation link in type optimization process improves model optimization performance, reduces model optimization difficulty, reduces model optimization process
In error rate.
To enable the above objects, features and advantages of the present invention to be clearer and more comprehensible, preferred embodiment is cited below particularly, and cooperate
Appended attached drawing, is described in detail below.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 is the frame structure signal using the electric terminal of model optimization method and apparatus provided in an embodiment of the present invention
Figure.
Fig. 2 is the flow diagram of model optimization method provided in an embodiment of the present invention.
Fig. 3 is another flow diagram of model optimization method provided in an embodiment of the present invention.
Fig. 4 is the frame structure schematic diagram of model optimization device provided in an embodiment of the present invention.
Icon: 10- electric terminal；100- model optimization device；110- hierarchical information obtains module；120- hierarchical classification mould
Block；The first merging module of 130-；140- first judgment module；The second merging module of 150-；160- third merging module；170-
Two judgment modules；300- memory；400- storage control；500- processor.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are some of the embodiments of the present invention, instead of all the embodiments.Usually herein
The component of the embodiment of the present invention described and illustrated in place's attached drawing can be arranged and be designed with a variety of different configurations.
Therefore, the detailed description of the embodiment of the present invention provided in the accompanying drawings is not intended to limit below claimed
The scope of the present invention, but be merely representative of selected embodiment of the invention.Based on the embodiments of the present invention, this field is common
Technical staff's every other embodiment obtained without creative efforts belongs to the model that the present invention protects
It encloses.
It should also be noted that similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, does not then need that it is further defined and explained in subsequent attached drawing.
As shown in Figure 1, the side of the electric terminal 10 for application model optimization method and apparatus provided in an embodiment of the present invention
Mount structure schematic diagram, the electric terminal 10 include model optimization device 100, memory 300, storage control 400 and processing
Device 500.Wherein, the memory 300, storage control 400, each element of processor 500 are directly or indirectly electric between each other
Property connection, to realize the transmission or interaction of data.For example, passing through one or more communication bus or signal wire between these elements
It realizes and is electrically connected.The model optimization device 100 includes that at least one can be stored in described deposit in the form of software or firmware
Software function module in reservoir 300 or in the operating system that is solidificated in the electric terminal 10.The processor 500 is in institute
It states and accesses the memory 300 under the control of storage control 400, for executing holding of storing in the memory 300
Row module, such as software function module and computer program etc. included by the model optimization device 100.
It is appreciated that structure shown in FIG. 1 is only to illustrate, the electric terminal 10 may also include more than shown in Fig. 1
Perhaps less component or with the configuration different from shown in Fig. 1.In addition, the electric terminal 10 can be but not limited to
Smart phone, PC (personal computer, PC), tablet computer, personal digital assistant (personal
Digital assistant, PDA), it is mobile internet surfing equipment (mobile Internet device, MID), Cloud Server, small-sized
Machine etc..
Further, as shown in Fig. 2, being the flow diagram of model optimization method provided in an embodiment of the present invention, the mould
Type optimization method is applied to above-mentioned electric terminal 10, below in conjunction with Fig. 2 to the specific steps of the model optimization method and
Process is described in detail.It should be understood that the model optimization method provided in the present embodiment not with steps described below and
The sequence of process is limitation.
Step S11 obtains the hierarchical information of each submodel in model to be processed；
Step S12 classifies according to the whether identical each submodel in the model to be processed of hierarchical information with shape
At multiple file packets to be processed, the hierarchical information for each submodel for including in each file packet to be processed is identical；
Step S13, for each file packet to be processed, based on mesh grid property to each in the file packet to be processed
Submodel is merged to obtain initial model file；
Step S14, judges whether the mesh mesh parameter in the initial model file meets the first preset need, if not
Meet, then follow the steps S15, conversely, then determining that the mesh grid property in the initial model file is not necessarily to optimize；
Step S15 calls optimisation strategy corresponding with the mesh mesh parameter to carry out the initial model file
Mesh grid optimization is to obtain first object file.
In the model optimization method provided in above-mentioned steps S11- step S15, hierarchical information is primarily based on to mould to be processed
Each submodel in type is classified, and carries out the conjunction of mesh grid to of a sort submodel is belonged to based on mesh grid property
And and then judge whether the mesh mesh parameter in the model after completing to merge needs to optimize, and when not meeting demand
Mesh grid optimization is carried out, to realize to the automation of multiple submodels, mass, the processing of standardized model optimization, is mentioned
High model optimization efficiency, reduces error probability when model optimization.
In detail, in step S11 and step S12, the model to be processed can be buildings model, relief model, tree
The threedimensional models such as wooden mold.It is made of one or more buildings models or in addition, the model to be processed can be by one
Or multiple relief models composition, the present embodiment is herein with no restrictions.The hierarchical information can include but is not limited to type of service,
Business demand information etc., for example, being built when the hierarchical information is type of service it is assumed that the model to be processed is one
Model is built, then including such as wall submodel, floor submodel, furniture submodel, garden submodel, pipeline in the buildings model
Multiple submodels such as submodel, then when being classified according to type of service this hierarchical information, it can be by multiple wall submodules
Type is divided in a file packet to be processed, is divided to floor submodel in one file packet to be processed ..., with formation pair
Answer multiple file packets to be processed of different service types.
It, can also be based on pre- in addition to the automatic classification of the above-mentioned submodel in model to be processed in actual implementation
If classification standard, the sort operation instruction by responding user realizes classification to multiple submodels, and the present embodiment is herein not
It repeats.
Further, in step s 13, it is contemplated that in model performance optimization, the mesh mesh parameter of model can be direct
Influence model performance, therefore, in the present embodiment, can based on mesh grid property to each submodel in file packet to be processed into
Row merges to obtain an initial model file.
Further, in step S14 and step S15, the mesh mesh parameter can include but is not limited to number of vertex
With face number, first preset need can flexibly be set according to the difference of the mesh mesh parameter.For example, in this reality
It applies in example, when the mesh mesh parameter includes number of vertex and face number, then the actual implementation process of step S14 can wrap
It includes: judging whether the number of vertex of the initial model file or face number are greater than the first preset value, if more than then calling and subtracting face strategy
The initial model file is carried out subtracting surface treatment, until the number of vertex or the face number are not more than first preset value,
Completion is subtracted into the initial model file of surface treatment as first object file.Wherein, first preset value can be according to practical need
Seek carry out flexible design, the present embodiment is herein with no restrictions.It should further be appreciated that according to the correspondence between number of vertex and face number
Relationship, first preset value is different, corresponding four vertex in a such as face, then, when mesh mesh parameter is number of vertex
When the first preset value be the mesh mesh parameter be face number when four times etc., the present embodiment is not particularly limited herein.
Further, in order to be further reduced the artificial participation in model optimization treatment process, model optimization is improved
Intelligence, as shown in figure 3, in the present embodiment, the model optimization method can comprise further steps S16- step S19, tool
Body is as follows.
Step S16, multiple material ball files corresponding to the first object file are merged to obtain material to be processed
Matter ball file；
Step S17 is carried out based on the material ball file to be processed multiple textures files corresponding to each material ball file
Merge to obtain textures file to be processed；
Step S18, judges whether the textures parameter of the textures file to be processed meets the second preset need, if discontented
Foot, thens follow the steps S19, conversely, then determining the first object file without optimizing；
Step S19 calls optimisation strategy corresponding with the textures parameter to optimize the textures file to be processed
Processing obtains the second file destination.
Step S16- step S19 is carried out based on the above-mentioned first object file completed after mesh mesh update, optimization
The merging of material ball and textures optimization, wherein since each submodel in each file packet to be processed has respectively corresponded one
Material ball file, and each material ball carries textures file, therefore while carrying out material ball file mergences, can be pasted
Map file merges to obtain textures file to be processed, and then is carried out model into one according to the textures file to be processed after merging
Step ground optimization processing.
In detail, in step S18- step S19, the textures parameter can include but is not limited to textures size,
The resolution ratio etc. of textures, second preset need can carry out flexible design according to the actual conditions of textures parameter.The present embodiment
In, it is assumed that the textures parameter is image resolution ratio, then the specific implementation process of step S17 may include: to judge the figure
As whether resolution ratio is greater than the second preset value, if more than second preset value, then to the image of the textures file to be processed
Resolution ratio, which optimizes, is adjusted so that the image resolution ratio of the textures file to be processed is not more than second preset value, will
The first object file of resolution adjustment is completed as the second file destination.In another example when the ruler that the textures parameter is textures
When very little size, then the specific implementation process of step S17 may include: to calculate the textures size of the textures file after merging, sentence
Whether disconnected textures size is more than preset value, if it does, being then adjusted optimization until textures are big to the textures size after merging
It is small to be no more than preset value.
It is understood that repeating for the submodel in other file packets to be processed and executing above-mentioned steps S13- step
Rapid S17, and then complete to optimize operation to the mass of model to be processed, details are not described herein for the present embodiment.In addition, due to each
Submodel be include that the file of tree of nodal information therefore can be by the file after optimization processing with model file
Form is exported, and such as can export (such as first object for the identical multiple submodels of hierarchical information as a model file
File, the second file destination), it can also be defeated as a model file using the corresponding multiple submodels of multiple and different hierarchical informations
Out etc..
Further, according to actual needs, before being exported to model, in order to enable model user can facilitate
Using the model after optimization, unified control interface can be provided the model after optimization, i.e., in the present embodiment, can further be pressed
The first object file or second file destination are named according to default naming rule.Specifically, it can be directed to each
The title of the nodes such as material ball file, textures file in hierarchical structure is carried out according to default naming rule (such as professional standard)
Name, the present embodiment is herein with no restrictions.
Based on the description to above-mentioned model optimization method, as shown in figure 4, the embodiment of the present invention also provides a kind of model optimization
Device 100, which is applied to the electric terminal 10, and the model optimization device 100 includes hierarchical information
Obtain module 110, hierarchical classification module 120, the first merging module 130, first judgment module 140, the second merging module 150,
Third merging module 160 and the second judgment module 170.
The hierarchical information obtains module 110, for obtaining the hierarchical information of each submodel in model to be processed；This reality
It applies in example, the description as described in the hierarchical information obtains module 110 specifically refers to the detailed description of above-mentioned steps S11, that is,
The step S11 can obtain module 110 by hierarchical information and execute, thus not illustrate more herein.
The hierarchical classification module 120, for according to the whether identical each son in the model to be processed of hierarchical information
Model is classified to form multiple file packets to be processed, the level for each submodel for including in each file packet to be processed
Information is identical；In the present embodiment, the description as described in the hierarchical classification module 120 specifically refers to the detailed of above-mentioned steps S12
Description, that is, the step S12 can be executed by hierarchical classification module 120, thus does not illustrate more herein.
First merging module 130, for be directed to each file packet to be processed, based on mesh grid property to it is described to
Each submodel in processing file packet is merged to obtain initial model file；In the present embodiment, merge about described first
The description of module 130 specifically refers to the detailed description of above-mentioned steps S13, that is, the step S13 can merge mould by first
Block 130 executes, thus does not illustrate more herein.
The first judgment module 140, for judging whether the mesh mesh parameter in the initial model file meets
First preset need, if not satisfied, then calling optimisation strategy corresponding with the mesh mesh parameter to the initial model text
Part carries out mesh grid optimization to obtain first object file.In addition, in the present embodiment, about the first judgment module 140
Description specifically refer to the detailed description of above-mentioned steps S14- step S15, that is, the step S14- step S15 can be by
First judgment module 140 executes, thus does not illustrate more herein.
Second merging module 150, for being closed to the corresponding multiple material ball files of the first object file
And to obtain material ball file to be processed；In the present embodiment, the description as described in second merging module 150 is specifically referred to
The detailed description of step S16 is stated, that is, the step S16 can be executed by the second merging module 150, thus is not made herein more
More explanations.
The third merging module 160, it is corresponding multiple to each material ball file based on the material ball file to be processed
Textures file is merged to obtain textures file to be processed；In the present embodiment, the description as described in the third merging module 160
The detailed description of above-mentioned steps S17 is specifically referred to, that is, the step S17 can be executed by third merging module 160, because
And do not illustrate more herein.
Second judgment module 170, for judging whether the textures parameter of the textures file to be processed meets second
Preset need, if not satisfied, optimisation strategy corresponding with the textures parameter is then called to carry out the textures file to be processed
Optimization is to obtain the second file destination.In the present embodiment, the description as described in second judgment module 170 specifically refers to above-mentioned
The detailed description of step S18- step S19, that is, the step S18- step S19 can be executed by the second judgment module 170,
Do not illustrate more herein thus.
In conclusion the embodiment of the present invention provides a kind of model optimization method and apparatus, wherein the present invention is based on unified
It is excellent can to effectively remove model to the mass of model, the optimization processing of automation for the model optimization standard implementation change, rationalized
Artificial participation link during change, improve model optimization performance, reduce model optimization difficulty, reduce model optimization during
Error rate.
In several embodiments provided by the embodiment of the present invention, it should be understood that disclosed device and method, it can also
To realize by another way.Device and method embodiment described above is only schematical, for example, in attached drawing
Flow chart and block diagram show that the devices of multiple embodiments according to the present invention, method and computer program product are able to achieve
Architecture, function and operation.In this regard, each box in flowchart or block diagram can represent module, a program
A part of section or code, a part of the module, section or code include that one or more is patrolled for realizing defined
Collect the executable instruction of function.It should also be noted that in some implementations as replacement, function marked in the box
It can occur in a different order than that indicated in the drawings.For example, two continuous boxes can actually be held substantially in parallel
Row, they can also be executed in the opposite order sometimes, and this depends on the function involved.It is also noted that block diagram and/or
The combination of each box in flow chart and the box in block diagram and or flow chart, can the function as defined in executing or dynamic
The dedicated hardware based system made is realized, or can be realized using a combination of dedicated hardware and computer instructions.
In addition, each functional module in each embodiment of the present invention can integrate one independent portion of formation together
Point, it is also possible to modules individualism, an independent part can also be integrated to form with two or more modules.
It, can be with if the function is realized and when sold or used as an independent product in the form of software function module
It is stored in a computer readable storage medium.Based on this understanding, technical solution of the present invention is substantially in other words
The part of the part that contributes to existing technology or the technical solution can be embodied in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be a
People's computer, electronic equipment or network equipment etc.) execute all or part of step of each embodiment the method for the present invention
Suddenly.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), deposits at random
The various media that can store program code such as access to memory (RAM, Random Access Memory), magnetic or disk.
It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to the packet of nonexcludability
Contain, so that the process, method, article or equipment for including a series of elements not only includes those elements, but also including
Other elements that are not explicitly listed, or further include for elements inherent to such a process, method, article, or device.
In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including the element
Process, method, article or equipment in there is also other identical elements.
The foregoing is merely alternative embodiments of the invention, are not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of model optimization method, which is characterized in that the model optimization method includes:
Obtain the hierarchical information of each submodel in model to be processed；
Classify according to the whether identical each submodel in the model to be processed of hierarchical information multiple to be processed to be formed
The hierarchical information of file packet, each submodel for including in each file packet to be processed is identical；
For each file packet to be processed, each submodel in the file packet to be processed is closed based on mesh grid property
And to obtain initial model file；
Judge whether the mesh mesh parameter in the initial model file meets the first preset need, if not satisfied, then calling
Optimisation strategy corresponding with the mesh mesh parameter carries out mesh grid optimization to the initial model file to obtain first
File destination.
2. model optimization method according to claim 1, which is characterized in that the mesh mesh parameter include number of vertex and
Face number, judges whether the mesh mesh parameter in the initial model file meets the first preset need, if not satisfied, then calling
Optimisation strategy corresponding with the mesh mesh parameter carries out mesh grid optimization to the initial model file to obtain first
The step of file destination, comprising:
Whether the number of vertex or face number for judging the initial model file are greater than the first preset value, if more than then calling and subtracting face plan
Slightly the initial model file is carried out subtracting surface treatment, until the number of vertex or the face number are default no more than described first
Completion is subtracted the initial model file of surface treatment as first object file by value.
3. model optimization method according to claim 1, which is characterized in that the model optimization method further include:
Multiple material ball files corresponding to the first object file are merged to obtain material ball file to be processed；
Merged based on the material ball file to be processed multiple textures files corresponding to each material ball file with obtain to
Handle textures file；
Judge whether the textures parameter of the textures file to be processed meets the second preset need, if not satisfied, then calling and institute
The corresponding optimisation strategy of textures parameter is stated to optimize to obtain the second file destination the textures file to be processed.
4. model optimization method according to claim 3, which is characterized in that the textures parameter includes image resolution ratio,
Judge whether the textures parameter of the textures file to be processed meets the second preset need, if not satisfied, then calling and the patch
The corresponding optimisation strategy of graph parameter optimizes the step of processing obtains the second file destination to the textures file to be processed, packet
It includes:
Judge whether described image resolution ratio is greater than the second preset value, if more than second preset value, then to described to be processed
The image resolution ratio of textures file optimizes the image resolution ratio for being adjusted so that the textures file to be processed no more than institute
The second preset value is stated, the first object file of resolution adjustment will be completed as the second file destination.
5. model optimization method according to claim 3, which is characterized in that the method also includes:
The first object file or second file destination are named according to default naming rule.
6. model optimization method according to claim 1, which is characterized in that the hierarchical information of each submodel includes industry
Service type.
7. a kind of model optimization device, which is characterized in that the model optimization device includes:
Hierarchical information obtains module, for obtaining the hierarchical information of each submodel in model to be processed；
Hierarchical classification module, for classifying according to the whether identical each submodel in the model to be processed of hierarchical information
To form multiple file packets to be processed, the hierarchical information for each submodel for including in each file packet to be processed is identical；
First merging module, for being directed to each file packet to be processed, based on mesh grid property to the file packet to be processed
In each submodel merge to obtain initial model file；
First judgment module, for judging whether the mesh mesh parameter in the initial model file meets the first default need
It asks, if not satisfied, then optimisation strategy corresponding with the mesh mesh parameter is called to carry out mesh to the initial model file
Grid optimization is to obtain first object file.
8. model optimization device according to claim 7, which is characterized in that the mesh mesh parameter include number of vertex and
Face number, it is default that the first judgment module is also used to judge whether the number of vertex of the initial model file or face number are greater than first
Value, if more than then calling subtracts face strategy and carries out subtracting surface treatment to the initial model file, until the number of vertex or the face
Number is not more than first preset value, and completion is subtracted the initial model file of surface treatment as first object file.
9. model optimization device according to claim 7, which is characterized in that the model optimization device further include:
Second merging module, for merging the corresponding multiple material ball files of the first object file to obtain wait locate
Manage material ball file；
Third merging module is carried out based on the material ball file to be processed multiple textures files corresponding to each material ball file
Merge to obtain textures file to be processed；
Second judgment module, for judging whether the textures parameter of the textures file to be processed meets the second preset need, if
It is unsatisfactory for, then calls optimisation strategy corresponding with the textures parameter to optimize processing to the textures file to be processed and obtain
Second file destination.
10. model optimization device according to claim 9, which is characterized in that the textures parameter includes image resolution ratio,
Second judgment module is also used to judge whether described image resolution ratio is greater than the second preset value, default if more than described second
Value, then optimize the figure for being adjusted so that the textures file to be processed to the image resolution ratio of the textures file to be processed
Picture resolution ratio is not more than second preset value, will complete the first object file of resolution adjustment as the second file destination.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811444028.9A CN109272567B (en) | 2018-11-29 | 2018-11-29 | Three-dimensional model optimization method and device |
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