CN111210505A - 3D model loading method, server, storage medium and processor - Google Patents
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Abstract
The invention discloses a 3D model loading method, a server, a storage medium and a processor, belonging to the technical field of virtual reality, wherein the 3D model loading method comprises the following steps: acquiring performance evaluation parameters of a terminal; the server optimizes the 3D model data requested to be loaded by the terminal according to the performance evaluation parameters; and the terminal loads the optimized 3D model data. According to the 3D model loading method disclosed by the invention, the 3D model data can be adaptively optimized according to different terminals, the technical problems that the 3D model is easy to block, collapse, drop and the like when the terminals are uniformly loaded in a flow manner in the prior art are solved, and the user experience is improved.
Description
Technical Field
The invention relates to the technical field of virtual reality, in particular to a 3D model loading method, a server, a storage medium and a processor.
Background
In the 3D model loading of the traditional equipment, the model data loading application is loaded by adopting a unified flow formula, and the unified flow loading is relatively stable because the early 3D model loading application field is limited to a PC (personal computer).
However, due to the popularization of the existing intelligent devices, different terminal devices have different parameter information, the 3D model is loaded in a unified flow, which often causes the situations of locking, collapse, disconnection and the like of the 3D model in the loading process due to insufficient performance of the terminal devices, so that the problems of incomplete 3D model obtained when a plurality of users load the 3D model, material loss and the like occur, the users have to repeatedly download the models for many times, or the performance of the terminal devices is insufficient to support the 3D model, so that the downloading time is too long, and the experience of the users is greatly reduced under the above situations.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a 3D model loading method, a server, a storage medium and a processor, so as to realize adaptive optimization of 3D model data according to different terminals, solve the technical problems that the unified flow loading of the 3D model by the terminals in the prior art is prone to jamming, collapse, disconnection and the like, and improve the user experience.
In a first aspect, an embodiment of the present invention provides a 3D model loading method, including:
acquiring performance evaluation parameters of a terminal;
the server optimizes the 3D model data requested to be loaded by the terminal according to the performance evaluation parameters;
and the terminal loads the optimized 3D model data.
With reference to the first aspect, an embodiment of the present invention provides a first possible implementation manner of the first aspect, where the obtaining a performance evaluation parameter of a terminal includes:
acquiring equipment parameters of the terminal;
and acquiring the performance evaluation parameters matched with the equipment parameters in a server database.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present invention provides a second possible implementation manner of the first aspect, where the device parameter includes hardware configuration information and/or software operating environment information.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present invention provides a third possible implementation manner of the first aspect, where the obtaining, in the server database, the performance evaluation parameter that matches the device parameter includes:
acquiring CPU performance evaluation parameters matched with the CPU parameters of the equipment parameters from a server database;
and acquiring GPU performance evaluation parameters matched with the GPU parameters of the equipment parameters in a server database.
With reference to the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, where the optimizing, by the server, the 3D model data requested to be loaded by the terminal according to the performance evaluation parameter includes:
acquiring the number of model bearable surfaces of the terminal according to the performance evaluation parameters;
acquiring the number of faces of the 3D model data requested to be loaded by the terminal;
comparing the number of the bearing surfaces of the model with the number of the surfaces of the 3D model data; if the number of the surfaces of the 3D model data is less than or equal to the number of the bearable surfaces of the model, adopting the 3D model data; and if the number of the surfaces of the 3D model data is larger than the number of the bearing surfaces of the model, performing surface reduction optimization processing on the 3D model data.
With reference to the fourth possible implementation manner of the first aspect, an embodiment of the present invention provides a fifth possible implementation manner of the first aspect, where the subtractive optimization process includes one of:
high-level reduction surface optimization processing;
medium level reduction optimization processing;
and (5) carrying out Low-level face reduction optimization processing.
With reference to the first aspect, an embodiment of the present invention provides a sixth possible implementation manner of the first aspect, where the loading, by the terminal, the optimized 3D model data includes:
acquiring a download configuration adapted to the performance evaluation parameter according to the performance evaluation parameter;
and the terminal receives the optimized 3D model data under the downloading configuration.
In a second aspect, an embodiment of the present invention provides a server, including:
the acquisition module is used for acquiring the performance evaluation parameters of the terminal;
the optimization module is used for optimizing the 3D model data requested to be loaded by the terminal according to the performance evaluation parameters;
and the transmission module is used for returning the optimized 3D model data to the terminal.
In a third aspect, an embodiment of the present invention provides a storage medium, where the storage medium includes a stored program, where the program executes any one of the methods described above.
In a fourth aspect, an embodiment of the present invention provides a processor, where the processor is configured to execute a program, where the program executes to perform any one of the methods described above.
According to the 3D model loading method provided by the invention, the performance evaluation parameters of the current terminal are obtained, the server optimizes the 3D model data requested to be loaded by the terminal according to the performance evaluation parameters of the current terminal, and the terminal loads the optimized 3D model data, so that the 3D model data requested to be loaded by the terminal is adaptively optimized according to the performances of different terminals, the 3D model data is matched with the information of user terminal equipment, specific optimization aiming at specific equipment is realized, and each user can use the optimal 3D model loading scheme at the terminal equipment. The technical problem that the situations of blocking, collapse, disconnection and the like easily occur when the 3D model is loaded in a unified terminal flow mode in the prior art is effectively solved, and the user experience is greatly improved.
Meanwhile, the adaptive downloading configuration is obtained according to the performance evaluation parameters of the terminal, the terminal receives the optimized 3D model data under the downloading configuration, namely, secondary optimization is carried out, the adaptive optimization is carried out on the 3D model data, and then specific downloading configuration is carried out on specific equipment in transmission, so that each user can use the optimal 3D model loading scheme on the terminal equipment. The technical problem that in the prior art, the downloading time is too long due to the fact that the performance of the terminal device is not enough to support the 3D model is effectively solved, and the user experience is greatly improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a first schematic flow chart of a 3D model loading method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart diagram of a 3D model loading method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a 3D model loading method provided in an embodiment of the present invention;
fig. 4 is a schematic diagram of a server provided in an embodiment of the present invention.
In the figure:
1. an acquisition module; 2. an optimization module; 3. and a transmission module.
Detailed Description
The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Example 1
As shown in fig. 1 to fig. 3, the present embodiment provides a 3D model loading method, including:
step S101: and acquiring the performance evaluation parameters of the terminal. For evaluating the performance of the current terminal.
Step S102: and the server optimizes the 3D model data requested to be loaded by the terminal according to the performance evaluation parameters. Namely, the 3D model data which is requested to be loaded is adaptively optimized according to the performance of the front terminal, so that the 3D model data is matched with the information of the user terminal equipment.
Step S103: and the terminal loads the optimized 3D model data.
In step S101, acquiring a performance evaluation parameter of the terminal includes:
step S1011: acquiring equipment parameters of a terminal;
step S1012: and acquiring the performance evaluation parameters matched with the equipment parameters in a server database.
Optionally, in step S1011, the terminal may include, but is not limited to, an android system mobile phone, a tablet computer, an IOS mobile phone, a tablet computer, a VR all-in-one machine (Oculus Go/Quest, Pico, and Roc VR), a Windows PC computer, a Linux PC, a MacOS PC, and the like.
Optionally, in step S1011, the device parameters include hardware configuration information and software operating environment information of the terminal, where the hardware configuration information includes CPU information (CPU is a central processing unit), GPU information (GPU is a graphics processing unit), memory information, network adapter information, software and hardware driver information, and the software operating environment information includes system driver, operating system information, system thread, installed network protocol, installed network client, and the like.
In step S1011, first, a current operating environment is requested, then an instruction requesting to read the device parameters of the user terminal is sent, and if the hardware configuration information and the software operating environment information of the user terminal are successfully acquired, the hardware configuration information and the software operating environment information of the user terminal are written into a cache document for temporary storage;
if the hardware configuration information and the software operation environment information of the terminal are acquired unsuccessfully, the steps are repeated (the current operation environment is requested, then an instruction for requesting to read the equipment parameters of the user terminal is sent), when the request is invalid for multiple times, a manual input module is called, the user needs to manually input the equipment parameters of the current terminal at the moment, and when the equipment parameters input by the user are confirmed, the storage equipment parameters are recorded.
The method for acquiring the device parameters of the terminal includes the following steps:
1) the PC program uses C # code to compile and sequentially obtains a CPU serial number, a network card hardware address, an IP address, a hard disk ID, user information, a PC system type, a physical memory and the like according to Win32 API, and the specific reference is as follows:
try {// obtain CPU serial number code string CPU info ═ "; v/cpu serial number management class ═ new management class ("Win32_ Processor"); getinstances (); force (managementobject mo in moc) { cpuInfo ═ mo. properties [ "ProcessorId" ]. value. tostring (); moc ═ null; mc is null; return cpuInfo; catch { return "unbnow"; }
try {// obtain network card hardware address string mac ═ in ═; management class mc ═ newmanagement class ("Win32_ network adapter configuration"); getinstances (); foreach (managementobjectmo in moc) { if ((pool) mo [ "ipenable" ] ═ true) { mac [ "MacAddress" ]. toshring (); break; } moc ═ null; mc is null; return mac; catch { return "unbnow"; }
try {// obtain IP address string st ═ in ═; management class mc ═ new management class ("Win32_ network adapter configuration"); getinstances (); foreach (managementobject mo in moc) { if ((pool) mo [ "ipenable" ] {// st ═ mo [ "IpAddress" ]. toshring (); system, array ar; ar ═ Value (system. array) (mo. properties [ "IpAddress" ]. Value); st ar, getvalue (0), tosring (); break; } moc ═ null; mc is null; return st; catch { return "unbnow"; }
try {// obtain hard disk ID String HDid ═; management class mc ═ newmanagement class ("Win32_ distkdrive"); getinstances (); foreach (managementobject mo in moc) { HDid ═ properties [ "Model" ]. Value; moc ═ null; mc is null; return HDid; catch { return "unbnow"; }
try {// obtain login user IDstring st ═ in; management class mc ═ newmanagement class ("Win32_ computer system"); getinstances (); foreach (managementobject mo in moc) { st ═ mo [ "UserName" ]. ToString (); moc ═ null; mc is null; return st; catch { return "unbnow"; }
try {// PC type string st ═ "; management class mc ═ new management class ("Win32_ computer system"); getinstances (); foreach (managementobject mo in moc) { st ═ mo [ "SystemType" ]. tostiging (); moc ═ null; mc is null; return st; catch { return "unbnow"; }
try {// physical memory type string st ═ in "; management class mc ═ new management class ("Win32_ computer system"); getinstances (); foreach (management object mo in moc) { st ═ mo [ "totalphysical memory" ]. ToString (); moc ═ null; mc is null; return st; catch { return "unbnow"; }
2) The android terminal can use android.
In step S1012, acquiring the performance evaluation parameter matched with the device parameter from the server database includes:
acquiring CPU performance evaluation parameters matched with the CPU parameters of the equipment parameters from a server database; comparing the performance data of the same CPU in a server database according to the CPU information in the acquired hardware configuration information of the terminal to acquire CPU performance evaluation parameters;
acquiring GPU performance evaluation parameters matched with GPU parameters of the equipment parameters from a server database; comparing performance data of the same GPU in a server database according to GPU information in the acquired hardware configuration information of the terminal to acquire GPU performance evaluation parameters;
after obtaining each performance evaluation parameter, storing the performance evaluation parameter in the local user information.
The method comprises the steps of obtaining a range interval of big data average network performance data of a CPU according to CPU information comparison, obtaining CPU performance evaluation parameters, obtaining a big data interval of writing speed through memory comparison, obtaining memory performance evaluation parameters, integrating information such as a network port and the like, and obtaining the performance evaluation parameters of a terminal according to the comprehensive analysis capacity of the CPU and the GPU of the terminal.
The server database is a big data cloud database, and can prestore information such as equipment parameters, CPU performance evaluation parameters, GPU performance evaluation parameters and the like of various terminals in the server database.
In step S102, the step of optimizing, by the server, the 3D model data requested to be loaded by the terminal according to the performance evaluation parameter includes:
step S1021: acquiring the number of model bearable surfaces of the terminal according to the performance evaluation parameters; the number of model bearing surfaces matched with the CPU performance evaluation parameter and the GPU performance evaluation parameter can be obtained in a server database according to the CPU performance evaluation parameter and the GPU performance evaluation parameter of the terminal, for example, 30-50W surfaces;
step S1022: acquiring the number of faces of 3D model data requested to be loaded by a terminal;
step S1023: comparing the number of the supportable bearing surfaces of the model with the number of the surfaces of the 3D model data; if the number of the surfaces of the 3D model data is less than or equal to the number of the bearing surfaces of the model, the 3D model data is adopted, namely the 3D model data is directly adopted without optimization, and the complete 3D model data is stored; and if the number of the surfaces of the 3D model data is larger than the number of the supportable bearing surfaces of the model, performing surface reduction optimization processing on the 3D model data.
In step S1023, the face reduction optimization process includes one of the following:
high-level reduction surface optimization processing;
medium level reduction optimization processing;
and (5) carrying out Low-level face reduction optimization processing.
And the face reduction optimization processing is to optimize the 3D model data by using an automatic face reduction tool (High-level face reduction optimization processing, Medium-level face reduction optimization processing and Low-level face reduction optimization processing, the effect after the optimization processing is reduced in sequence), sequentially comparing the number of the model bearing surfaces of the terminal according to three optimization schemes of the High-level face reduction optimization processing, the Medium-level face reduction optimization processing and the Low-level face reduction optimization processing, obtaining the highest effect in the number of the model bearing surfaces of the terminal, and automatically applying the Low-level face reduction optimization processing with the lowest effect if the three optimization schemes are not met.
The surface reduction optimization processing principle is as follows:
the vertices and faces are preprocessed to generate the following structure:
after generation, the cost of each vertex collapsing to each neighbor vertex is calculated according to the information, the collapse from a to b is to eliminate a, and a in each neighbor edge of a is replaced by b.
The formula is cost with the collapse from u to v:
tu is the set of triangles that contain vertex u;
tuv is a set of triangles that contains both vertex u and vertex v.
The closer the normal multiplication of the two surfaces is to 1, the closer the two surfaces are parallel, the smaller the elimination effect is, in the formula, the range is mapped from-1, 1 to 0, 1 by 1-x/2, and the smaller the value is, the smaller the elimination effect is. At the innermost layer, find the minimum of influence, for triangles containing vertex u and vertex v, elimination means that these faces disappear; at the outer layer, find the maximum of the influence, i.e. find the maximum influence on vision that contains u but does not disappear. Finally multiplied by the distance, the larger the distance, the less susceptible to erasure. And eliminating the node with the minimum cost, and paying attention to the instant update of the structure generated in the first step. And continuously eliminating until the number of the top points is less than a certain value.
In this embodiment, the High-level surface reduction optimization is performed until the number of vertices is smaller than 80% of the original 3D model data, the Medium-level surface reduction optimization is performed until the number of vertices is smaller than 70% of the original 3D model data, and the Low-level surface reduction optimization is performed until the number of vertices is smaller than 60% of the original 3D model data.
In step S103, the terminal loads the optimized 3D model data, including:
step S1031: acquiring a download configuration adapted to the performance evaluation parameter according to the performance evaluation parameter; according to the performance evaluation parameters corresponding to the hardware configuration information and the performance evaluation parameters corresponding to the software operation environment information in the performance evaluation parameters, the download configuration of the terminal matched with the performance evaluation parameters is obtained from the background cloud database, and the optimal download configuration of the approximate terminal matched with the performance evaluation parameters is realized from the background cloud database.
Wherein the download configuration includes bandwidth and download speed.
Step S1032: and the terminal receives the optimized 3D model data under the downloading configuration and displays the 3D model data on the terminal. The data flow of the 3D model is optimized, and the data flow is stable and free of blockage in the transmission process.
As can be seen from the above, in the 3D model loading method provided in this embodiment, the server optimizes the 3D model data requested to be loaded by the terminal according to the performance evaluation parameter of the current terminal, and the terminal loads the optimized 3D model data, so as to invoke different optimization processes according to the performances of different terminals to adaptively optimize the 3D model data requested to be loaded by the terminal, so that the 3D model data matches with the information of the user terminal device, thereby implementing specific optimization for specific devices, and enabling each user to use an optimal 3D model loading scheme at its own terminal device. The technical problem that the situations of blocking, collapse, disconnection and the like easily occur when the 3D model is loaded in a unified terminal flow mode in the prior art is effectively solved, and the user experience is greatly improved. Meanwhile, the adaptive downloading configuration is obtained according to the performance evaluation parameters of the terminal, the terminal receives the optimized 3D model data under the downloading configuration, namely, secondary optimization is carried out, the adaptive optimization is carried out on the 3D model data, and then specific downloading configuration is carried out on specific equipment in transmission, so that each user can use the optimal 3D model loading scheme on the terminal equipment. The technical problem that in the prior art, the downloading time is too long due to the fact that the performance of the terminal device is not enough to support the 3D model is effectively solved, and the user experience is greatly improved.
Example 2
As shown in fig. 4, the present embodiment provides a server including:
-an obtaining module for obtaining performance evaluation parameters of the terminal;
the program operation firstly requests a current operation environment, then sends an instruction for requesting to read the equipment parameters of the terminal, if the hardware configuration information and the software operation environment information of the user terminal are successfully acquired, the hardware configuration information and the software operation environment information of the user terminal are written into a cache document for temporary storage, if the hardware configuration information and the software operation environment information of the terminal are failed to be acquired, the steps (requesting the current operation environment and then sending the instruction for requesting to read the equipment parameters of the user terminal) are repeated, a manual input module is called after the multiple requests are invalid, the user needs to manually input the equipment parameters of the current terminal at the moment, and the storage equipment parameters are recorded after the user inputs the equipment parameters;
the program immediately sends the equipment parameters to the server database after acquiring the equipment parameters of the user terminal, the server database receives the equipment parameters, processes and compares the equipment parameters and then returns the performance evaluation parameters, and the program stores the performance evaluation parameters of the terminal in the local user information.
-an optimization module for optimizing the 3D model data requested to be loaded by the terminal according to the performance evaluation parameters;
after receiving the 3D model data to be imported selected by the user, the server performs corresponding optimization processing on the 3D model data according to the performance evaluation parameters stored by the user, and stores the optimized 3D model data.
-a transmission module for returning the optimized 3D model data to the terminal;
according to the performance evaluation parameters of the terminal, the optimized 3D model data is automatically matched with the node port gateway information, the optimal bandwidth and the downloading speed are adapted, and the downloading experience of a user is optimized.
The automatic matching of the node port gateway information adapts to the optimal bandwidth and download speed, and the principle is as follows:
the optimization of the transmission efficiency of the resource data requires that the data is packaged and transmitted, the network resource data is analyzed in a progressive grading mode, the rule matching and filtering are carried out on the network resource data by adopting a BM mode matching algorithm, the intercepted network resource data are analyzed step by step according to the format of the network resource data and each transmission protocol format, the analysis and filtering of the network resource data are realized, and the packaging and transmitting of the network resource data are realized on the basis.
P info＝{P ip，P pt，P p，P data，P other}
In the formula, P IP represents a network IP address, P pt represents a network port number, P P represents a network resource data transfer protocol type, P data represents network communication data, and P other represents other information of the network. In the process of transmitting the network resource data packet, the P ip, the P P P and the P data are necessary data for realizing the monitoring and control of the network resource data.
The IP address ensures effective transmission of network resource packets, for each network resource data P, the IP address P IP is f IP (P), f IP represents a network IP address parsing method, each transmitted network resource packet includes a destination port and a source port, the port number P pt is f pt (P), f pt represents a port number parsing method, the network resource data transfer protocol P P is f P (P), f P represents a transfer protocol parsing method, for each P, there is a corresponding P P and its corresponding, and each network resource data P includes protocol information. For each network resource data P, the data Pdata is f data (P), wherein f data represents the parsing method of the network resource data, different syntax rules and coding forms exist for different network resource data transmission protocols, and if the different data transmission protocols f data are different, there are
f data＝{f http，f smtp，f pop3，f ftp，…}
In the formula, f http represents an analysis method of network resource data, f SMTP represents an analysis method of network resource data of an SMTP transfer protocol, f POP3 represents an analysis method of resource data of a POP3 protocol, and f FTP represents an analysis method of resource data of an FTP protocol. The network resource data rule Rdata corresponds to different network data packet transmission protocols to set different rules, and the following formula is used to define a rule set
R＝{Rip，Rpt，Rp，Rdata}
In the formula, Rip represents a network resource data packet transmission IP address rule, Rpt represents a port number rule, Rp represents a network resource data packet transmission protocol rule, and Rdata represents a network resource data rule. The matching rule is used for realizing the purpose of filtering network resource data packets so as to achieve communication, and network resource data obtained by analysis at each level is matched with the corresponding rule, so that the data packet transmission in the network resources is completed. According to the principle, the optimization of the data transmission efficiency of the network resources is realized.
Example 3
The present embodiment provides a storage medium comprising a stored program, wherein the program when executed performs any of the methods described above.
Alternatively, in the present embodiment, the storage medium may be configured as a program code for performing the following steps:
s1, acquiring performance evaluation parameters of the terminal;
s2, optimizing the 3D model data requested to be loaded by the terminal according to the performance evaluation parameters;
and S3, matching the download configuration according to the performance evaluation parameters.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing program codes, such as a usb disk, a Read-only Memory (ROM), a Random Access Memory (RAMO), a mobile hard disk, a magnetic disk, or an optical disk.
The present embodiment also provides a processor for executing a program, where the program executes to perform the steps in any of the above methods.
Optionally, in this embodiment, the program is configured to perform the following steps:
s1, acquiring performance evaluation parameters of the terminal;
s2, optimizing the 3D model data requested to be loaded by the terminal according to the performance evaluation parameters;
and S3, matching the download configuration according to the performance evaluation parameters.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A3D model loading method is characterized by comprising the following steps:
acquiring performance evaluation parameters of a terminal;
the server optimizes the 3D model data requested to be loaded by the terminal according to the performance evaluation parameters;
and the terminal loads the optimized 3D model data.
2. The 3D model loading method according to claim 1, wherein the obtaining of the performance evaluation parameters of the terminal includes:
acquiring equipment parameters of the terminal;
and acquiring the performance evaluation parameters matched with the equipment parameters in a server database.
3. The 3D model loading method according to claim 2, characterized in that:
the device parameters include hardware configuration information and/or software operating environment information.
4. The 3D model loading method according to claim 2, wherein the obtaining of the performance evaluation parameter matching the device parameter in the server database comprises:
acquiring CPU performance evaluation parameters matched with the CPU parameters of the equipment parameters from a server database;
and acquiring GPU performance evaluation parameters matched with the GPU parameters of the equipment parameters in a server database.
5. The 3D model loading method according to claim 1, wherein the server optimizes the 3D model data requested to be loaded by the terminal according to the performance evaluation parameter, comprising:
acquiring the number of model bearable surfaces of the terminal according to the performance evaluation parameters;
acquiring the number of faces of the 3D model data requested to be loaded by the terminal;
comparing the number of the bearing surfaces of the model with the number of the surfaces of the 3D model data; if the number of the surfaces of the 3D model data is less than or equal to the number of the bearable surfaces of the model, adopting the 3D model data; and if the number of the surfaces of the 3D model data is larger than the number of the bearing surfaces of the model, performing surface reduction optimization processing on the 3D model data.
6. The 3D model loading method of claim 5, wherein the subtractive optimization process comprises one of:
high-level reduction surface optimization processing;
medium level reduction optimization processing;
and (5) carrying out Low-level face reduction optimization processing.
7. The 3D model loading method according to any one of claims 1 to 6, wherein the loading of the optimized 3D model data by the terminal comprises:
acquiring a download configuration adapted to the performance evaluation parameter according to the performance evaluation parameter;
and the terminal receives the optimized 3D model data under the downloading configuration.
8. A server, comprising:
the acquisition module is used for acquiring the performance evaluation parameters of the terminal;
the optimization module is used for optimizing the 3D model data requested to be loaded by the terminal according to the performance evaluation parameters;
and the transmission module is used for returning the optimized 3D model data to the terminal.
9. A storage medium, comprising a stored program, wherein the program when executed performs the method of any one of claims 1 to 7.
10. A processor for running a program, wherein,
the program when running performs the method of any one of claims 1 to 7.
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