Seat decoration recommendation method and device for shopping mall

Abstract

The invention provides a seat decoration recommendation method and device for a shopping mall. The seat decoration recommendation method of the shopping mall comprises the following steps: receiving a plurality of images of an automobile seat; constructing a three-dimensional model of the automobile seat by using the plurality of images; receiving a first text for seat decoration search; based on the first text, utilizing a trained deep neural network to determine a recommended seat decoration corresponding to the first text; and rendering the recommended seat decoration corresponding to the first text on the three-dimensional model of the automobile seat to generate a rendered image. According to the method, the three-dimensional model, determined by the user, of the to-be-decorated automobile seat is utilized, so that the user can check the effect that the seat decoration is installed on the seat, the real decoration effect after the user purchases the seat decoration can be reflected to the maximum extent, and then the occurrence of goods refunding and changing caused by the poor decoration effect after shopping is reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of information technology, and in particular to a method and device for recommending seat decorations in a shopping mall. Background Art

[0002] In the current shopping experience of seat decoration in shopping malls, the decoration effect of existing seats provided by merchants or the seat decoration effect displayed in live broadcast rooms is usually used as a reference. However, in the actual shopping experience, the seat decoration that looks good on the existing seats is found to have completely different decoration effects after being installed on the user's own car seat, resulting in returns and exchanges. Therefore, further improvements in this regard are expected. Summary of the invention

[0003] In order to solve the existing problems, the present invention provides a method and device for recommending seat decorations in a shopping mall, a terminal and a storage medium.

[0004] The present disclosure adopts the following technical solutions.

[0005] An embodiment of the present disclosure provides a seat decoration recommendation method for a shopping mall, the seat decoration recommendation method for a shopping mall comprising: receiving multiple images of car seats; constructing a three-dimensional model of the car seat using the multiple images; receiving a first text for seat decoration search; based on the first text, determining recommended seat decorations corresponding to the first text using a trained deep neural network, wherein the trained deep neural network is obtained by the following steps: constructing a deep neural network; receiving a second text for seat decoration search; based on the second text, determining multiple sets of seat decorations corresponding to the second text, and rendering the multiple sets of seat decorations corresponding to the second text on the three-dimensional model of the car seat respectively; in response to selection of a set of seat decorations among the multiple sets of seat decorations, determining the set of seat decorations as the recommended seat decoration corresponding to the second text; rendering the recommended seat decoration corresponding to the first text on the three-dimensional model of the car seat to generate a rendered image.

[0006] An embodiment of the present disclosure provides a seat decoration recommendation device for a shopping mall, the seat decoration recommendation device for the shopping mall comprising: an image receiving module, configured to receive multiple images of car seats; a three-dimensional model building module, configured to build a three-dimensional model of the car seat using the multiple images; a text receiving module, configured to receive a first text for seat decoration search; a seat decoration determination module, configured to determine, based on the first text, recommended seat decorations corresponding to the first text using a trained deep neural network, wherein the trained deep neural network is obtained by the following steps: constructing a deep neural network; receiving a second text for seat decoration search; based on the second text, determining multiple sets of seat decorations corresponding to the second text, and rendering the multiple sets of seat decorations corresponding to the second text on the three-dimensional model of the car seat respectively; in response to selection of a set of seat decorations among the multiple sets of seat decorations, determining the set of seat decorations as the recommended seat decoration corresponding to the second text; an image generation module, configured to render the recommended seat decoration corresponding to the first text on the three-dimensional model of the car seat to generate a rendered image.

[0007] In some embodiments, the present disclosure provides a seat decoration recommendation device for a shopping mall, comprising: at least one memory and at least one processor; wherein the memory is used to store program code, and the processor is used to call the program code stored in the memory to execute the above-mentioned seat decoration recommendation method for the shopping mall.

[0008] The present invention constructs a three-dimensional model of a user's car seat to be decorated in a shopping mall. After the user searches for car seat decorations, a trained deep neural network is used to determine recommended seat decorations corresponding to a first text, and the recommended seat decorations are rendered on the three-dimensional model of the corresponding car seat to generate a rendered image. In this way, using the three-dimensional model of the car seat to be decorated determined by the user, the user can view the effect of the seat decoration installed on the seat, which can reflect the actual decoration effect after the user's purchase to the greatest extent, thereby reducing the occurrence of returns and exchanges due to poor decoration effects after shopping. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0010] Figure 1 4 is a flowchart of a method for recommending seat decorations in a shopping mall according to an embodiment of the present disclosure.

[0011] Figure 2 Detailed description of the invention The invention is a flowchart of obtaining a trained neural network model according to an embodiment of the present invention.

[0012] Figure 3 4 is a flowchart of a method for recommending seat decorations in a shopping mall according to another embodiment of the present disclosure.

[0013] Figure 4 It is a partial module of a seat decoration recommendation device for a shopping mall according to another embodiment of the present disclosure.

[0014] Figure 5 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0015] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0016] It should be understood that the various steps described in the method embodiments of the present disclosure can be performed in sequence and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0017] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0018] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0019] It should be noted that the modification of “one” mentioned in the present disclosure is illustrative rather than restrictive, and those skilled in the art should understand that it should be understood as “one or more” unless otherwise clearly indicated in the context.

[0020] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0021] Figure 1A flow chart of a seat decoration recommendation method for a shopping mall of an embodiment of the present disclosure is provided. The seat decoration recommendation method for a shopping mall of the present disclosure may include step S101, receiving multiple images of a car seat. In some embodiments, the multiple images of the car seat may be acquired by a mobile phone or any suitable RGB camera, etc. In some embodiments, the multiple images are multiple images of the car seat at different angles. In some embodiments, the multiple images include 4 images. For example, in some embodiments, the multiple images include 4 images taken from the front, back, left and right sides of the car seat, respectively. This is conducive to the construction of a three-dimensional model. In some embodiments, the multiple images include a full-body image of the car seat. In this way, the constructed three-dimensional model can better reflect the decorative effect of the seat decoration.

[0022] In some embodiments, the method of the present disclosure may further include step S102, constructing a three-dimensional model of a car seat using multiple images. In some embodiments, there are currently many neural networks for constructing three-dimensional models, and any existing neural network model can be used to construct a three-dimensional model of a car seat. Of course, a new neural network model can also be created to construct a three-dimensional model of a car seat, which will not be repeated here for the sake of simplicity.

[0023] In some embodiments, the method of the present disclosure may further include step S103, receiving a first text for seat decoration search. In some embodiments, the first text includes the seat decoration type, such as seat cover, etc. In some embodiments, the first text may also include material, color and / or size. In this way, seat decoration recommendation can be made more accurately.

[0024] In some embodiments, the method of the present disclosure may further include step S104, based on the first text, using a trained deep neural network to determine a recommended seat decoration corresponding to the first text. Figure 2 As shown, the trained deep neural network is obtained through the following steps: step S201, constructing a deep neural network; step S202, receiving a second text for seat decoration search; step S203, based on the second text, determining multiple sets of seat decorations corresponding to the second text, and rendering the multiple sets of seat decorations corresponding to the second text on the three-dimensional model of the car seat respectively; step S204, in response to the selection of a set of seat decorations among the multiple sets of seat decorations, determining a set of seat decorations as the recommended seat decoration corresponding to the second text. After training, the obtained trained neural network model can determine the recommended seat decoration corresponding to the corresponding search text based on the user's search text.

[0025] In some embodiments, the method of the present disclosure may further include step S105, rendering the recommended seat decoration corresponding to the first text on the three-dimensional model of the car seat to generate a rendered image. In some embodiments, rendering the recommended seat decoration on the three-dimensional model may adopt a commonly used neural radiation field, which is not repeated here for the purpose of simplicity.

[0026] The present invention constructs a three-dimensional model of a user's car seat to be decorated in a shopping mall. After the user searches for car seat decorations, a trained deep neural network is used to determine recommended seat decorations corresponding to a first text, and the recommended seat decorations are rendered on the three-dimensional model of the corresponding car seat to generate a rendered image. In this way, using the three-dimensional model of the car seat to be decorated determined by the user, the user can view the effect of the seat decoration installed on the seat, which can reflect the actual decoration effect after the user's purchase to the greatest extent, thereby reducing the occurrence of returns and exchanges due to poor decoration effects after shopping.

[0027] In some embodiments, the seat decoration recommendation method of the shopping mall disclosed in the present invention may further include: displaying a rendered image on a search result page for seat decoration search. In this way, the user can intuitively view the decorative effect of the corresponding seat decoration. If the user is not satisfied, he or she can also select other seat decorations to try the decorative effect.

[0028] In some embodiments, in order to facilitate the purchase of seat decorations for family and friends' car seats in a shopping mall, the three-dimensional models of family and friends' car seats can also be stored in the shopping mall, and when purchasing seat decorations, the corresponding three-dimensional model can be selected to view the decoration effect. For example, in some embodiments, the seat decoration recommendation method of the shopping mall disclosed in the present invention can also include: step S301, receiving multiple images of another car seat; step S302, using multiple images of another car seat to construct a three-dimensional model of another car seat; step S303, receiving a third text for seat decoration search and the selection of a three-dimensional model of another car seat. It should be understood that when the shopping mall includes multiple three-dimensional models, the three-dimensional model of which car seat is to be decorated can be determined by selecting the three-dimensional model; step S304, based on the third text, using a trained deep neural network to determine the recommended seat decoration corresponding to the third text; step S305, rendering the recommended seat decoration corresponding to the third text on the three-dimensional model of another car seat to generate another rendered image.

[0029] The embodiment of the present disclosure further provides a seat decoration recommendation device 400 for a shopping mall. Figure 4The seat decoration recommendation device 400 of a shopping mall according to some embodiments is shown. The seat decoration recommendation device 400 of the shopping mall includes an image receiving module 401, a three-dimensional model building module 402, a text receiving module 403, a seat decoration determination module 404, and an image generation module 405. In some embodiments, the image receiving module 401 is configured to receive multiple images of a car seat. In some embodiments, the three-dimensional model building module 402 is configured to build a three-dimensional model of a car seat using multiple images. In some embodiments, the text receiving module 403 is configured to receive a first text for seat decoration search. In some embodiments, the seat decoration determination module 404 is configured to determine the recommended seat decoration corresponding to the first text using a trained deep neural network based on the first text, wherein the trained deep neural network is obtained by the following steps: constructing a deep neural network; receiving a second text for seat decoration search; based on the second text, determining multiple sets of seat decorations corresponding to the second text, and rendering the multiple sets of seat decorations corresponding to the second text on the three-dimensional model of the car seat respectively; in response to the selection of a set of seat decorations among the multiple sets of seat decorations, determining a set of seat decorations as the recommended seat decoration corresponding to the second text. In some embodiments, the image generation module 405 is configured to render the recommended seat decoration corresponding to the first text on the three-dimensional model of the car seat to generate a rendered image.

[0030] It should be understood that the contents described about the seat decoration recommendation method for a shopping mall are also applicable to the seat decoration recommendation device 400 for a shopping mall herein, and for the sake of simplicity, a detailed description is not given here.

[0031] In some embodiments, the multiple images are multiple images of the car seat at different angles. In some embodiments, the multiple images include 4 images. In some embodiments, the multiple images include a full-body image of the car seat. In some embodiments, the first text and the second text both include the seat decoration type. In some embodiments, the first text also includes color and / or size. In some embodiments, the seat decoration recommendation device of the shopping mall also includes: an image display module configured to display a rendered image on a search result page for seat decoration search. In some embodiments, the image receiving module is further configured to receive multiple images of another car seat; the three-dimensional model building module is further configured to construct a three-dimensional model of another car seat using multiple images of another car seat; the text receiving module is further configured to receive a third text for seat decoration search and a selection of a three-dimensional model of another car seat; the seat decoration determination module is further configured to determine the recommended seat decoration corresponding to the third text based on the third text using a trained deep neural network; the image generation module is further configured to render the recommended seat decoration corresponding to the third text on the three-dimensional model of another car seat to generate another rendered image.

[0032] In addition, the present disclosure also provides a terminal, including: at least one memory and at least one processor; wherein the memory is used to store program codes, and the processor is used to call the program codes stored in the memory to execute the above-mentioned shopping mall seat recommendation method.

[0033] In addition, the present disclosure also provides a computer storage medium, which stores program codes, and the program codes are used to execute the above-mentioned seat decoration recommendation method for the shopping mall.

[0034] The above describes the seat decoration recommendation method and device for a shopping mall disclosed in the present invention based on the embodiments and application examples. In addition, the present invention also provides a terminal and a storage medium, which are described below.

[0035] Reference below Figure 5 , which shows a schematic diagram of the structure of an electronic device (such as a terminal device or a server) 500 suitable for implementing the embodiment of the present disclosure. The terminal device in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0036] like Figure 5 As shown, the electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0037] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 5The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0038] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0039] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0040] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0041] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0042] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method of the present disclosure.

[0043] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0044] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0045] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0046] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0047] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0048] According to one or more embodiments of the present disclosure, a seat decoration recommendation method for a shopping mall is provided, the seat decoration recommendation method for a shopping mall comprising: receiving multiple images of car seats; constructing a three-dimensional model of the car seat using the multiple images; receiving a first text for seat decoration search; based on the first text, determining recommended seat decorations corresponding to the first text using a trained deep neural network, wherein the trained deep neural network is obtained by the following steps: constructing a deep neural network; receiving a second text for seat decoration search; based on the second text, determining multiple sets of seat decorations corresponding to the second text, and rendering the multiple sets of seat decorations corresponding to the second text on the three-dimensional model of the car seat respectively; in response to selection of a set of seat decorations among the multiple sets of seat decorations, determining the set of seat decorations as the recommended seat decoration corresponding to the second text; rendering the recommended seat decoration corresponding to the first text on the three-dimensional model of the car seat to generate a rendered image.

[0049] According to one or more embodiments of the present disclosure, the multiple images are multiple images of the car seat at different angles.

[0050] According to one or more embodiments of the present disclosure, the plurality of images include 4 images.

[0051] According to one or more embodiments of the present disclosure, the plurality of images include a full-body image of the car seat.

[0052] According to one or more embodiments of the present disclosure, the first text and the second text both include a seat decoration type.

[0053] According to one or more embodiments of the present disclosure, the first text further includes color and / or size.

[0054] According to one or more embodiments of the present disclosure, the seat decoration recommendation method of the shopping mall further includes: displaying the rendered image on a search result page for seat decoration search.

[0055] According to one or more embodiments of the present disclosure, the seat decoration recommendation method of the shopping mall also includes: receiving multiple images of another car seat; constructing a three-dimensional model of the other car seat using the multiple images of the other car seat; receiving a third text for seat decoration search and a selection of the three-dimensional model of the other car seat; based on the third text, determining a recommended seat decoration corresponding to the third text using the trained deep neural network; rendering the recommended seat decoration corresponding to the third text on the three-dimensional model of the other car seat to generate another rendered image.

[0056] According to one or more embodiments of the present disclosure, a seat decoration recommendation device for a shopping mall is provided, and the seat decoration recommendation device for the shopping mall includes: an image receiving module, configured to receive multiple images of car seats; a three-dimensional model building module, configured to build a three-dimensional model of the car seat using the multiple images; a text receiving module, configured to receive a first text for seat decoration search; a seat decoration determination module, configured to determine the recommended seat decoration corresponding to the first text based on the first text using a trained deep neural network, wherein the trained deep neural network is obtained by the following steps: constructing a deep neural network; receiving a second text for seat decoration search; based on the second text, determining multiple sets of seat decorations corresponding to the second text, and rendering the multiple sets of seat decorations corresponding to the second text on the three-dimensional model of the car seat respectively; in response to selection of a set of seat decorations among the multiple sets of seat decorations, determining the set of seat decorations as the recommended seat decoration corresponding to the second text; an image generation module, configured to render the recommended seat decoration corresponding to the first text on the three-dimensional model of the car seat to generate a rendered image.

[0057] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0058] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0059] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A method for recommending seat decorations in a shopping mall, characterized in that: The seat decoration recommendation method of the shopping mall includes: receiving a plurality of images of a car seat; constructing a three-dimensional model of the vehicle seat using the plurality of images; receiving a first text for a seat decoration search; Based on the first text, a recommended seat decoration corresponding to the first text is determined using a trained deep neural network, wherein the trained deep neural network is obtained by the following steps: constructing a deep neural network; receiving a second text for seat decoration search; based on the second text, determining multiple sets of seat decorations corresponding to the second text, and rendering the multiple sets of seat decorations corresponding to the second text on the three-dimensional model of the car seat respectively; in response to a selection of a set of seat decorations from the multiple sets of seat decorations, determining the set of seat decorations as the recommended seat decoration corresponding to the second text; The recommended seat decoration corresponding to the first text is rendered on the three-dimensional model of the car seat to generate a rendering image.

2. The method for recommending seat decorations in a shopping mall according to claim 1, characterized in that: The multiple images are multiple images of the car seat at different angles.

3. The method for recommending seat decorations in a shopping mall according to claim 2, characterized in that: The plurality of images include 4 images.

4. The method for recommending seat decorations in a shopping mall according to claim 1, characterized in that: The plurality of images includes a full body image of the vehicle seat.

5. The method for recommending seat decorations in a shopping mall according to claim 1, characterized in that: The first text and the second text both include a mount type.

6. The method for recommending seat decorations in a shopping mall according to claim 5, characterized in that: The first text also includes color and / or size.

7. The method for recommending seat decorations in a shopping mall according to claim 1, characterized in that: Also includes: The rendered image is displayed on a search results page for a seat trim search.

8. The method for recommending seat decorations in a shopping mall according to claim 1, characterized in that: Also includes: receiving a plurality of images of another vehicle seat; constructing a three-dimensional model of the other vehicle seat using the multiple images of the other vehicle seat; receiving a third text for a seat trim search and a selection of the three-dimensional model of another vehicle seat; Based on the third text, determining a recommended seat decoration corresponding to the third text using the trained deep neural network; The recommended seat decoration corresponding to the third text is rendered on the three-dimensional model of the other vehicle seat to generate another rendering image.

9. A seat decoration recommendation device for a shopping mall, characterized in that: The seat decoration recommendation device of the shopping mall includes: an image receiving module configured to receive a plurality of images of a vehicle seat; a three-dimensional model building module, configured to build a three-dimensional model of the vehicle seat using the multiple images; A text receiving module configured to receive a first text for seat decoration search; A seat decoration determination module is configured to determine, based on the first text, a recommended seat decoration corresponding to the first text using a trained deep neural network, wherein the trained deep neural network is obtained by the following steps: constructing a deep neural network; receiving a second text for seat decoration search; based on the second text, determining a plurality of sets of seat decorations corresponding to the second text, and rendering the plurality of sets of seat decorations corresponding to the second text on the three-dimensional model of the car seat respectively; in response to a selection of a set of seat decorations among the plurality of sets of seat decorations, determining the set of seat decorations as a recommended seat decoration corresponding to the second text; The image generation module is configured to render the recommended seat decoration corresponding to the first text on the three-dimensional model of the car seat to generate a rendered image.

10. A seat decoration recommendation device for a shopping mall, comprising: at least one memory and at least one processor; The at least one memory is used to store program codes, and the at least one processor is used to call the program codes stored in the at least one memory to execute the method for recommending seat decorations for a shopping mall according to any one of claims 1 to 8.

Images