HK40119837A - Indoor layout support method, system, and program - Google Patents

Description

Interior layout support methods, systems and procedures

Technical Field

This invention relates to an interior layout support method, system, and procedure for studying the layout of goods in an interior space.

Background Technology

For example, in real estate-related businesses such as construction, renovation, and moving, where safety and accuracy are paramount, drawing design plans and site plans is an essential responsibility. Typically, these are created by architects and CAD experts.

In this case, the site is first measured, and a scaled-down floor plan is drawn up based on the measurement results. Special symbols, values, materials, colors, etc. are written on the floor plan. This task alone takes about one to two weeks.

Existing technical documents

Patent documents

Patent Document 1: Japanese Patent Application Publication No. 2021-012528

Summary of the Invention

The problem to be solved

The drawings and instructions produced in this way become a three-dimensional impression in the minds of those who see them, and construction, wiring, object placement, coloring, and other tasks are carried out according to the drawn dimensions.

Because the construction or renovation of buildings involves a large number of practitioners, each practitioner carefully checks each other's work to ensure that their three-dimensional impressions are accurate.

In addition, each practitioner carries dozens of drawn paper drawings and instructions while working, thus requiring a large amount of paper.

Therefore, traditional construction sites suffer from a significant waste of manpower, time, and resources.

In addition, there is a need to efficiently select and purchase home appliances, furniture, and other goods based on interior CAD data.

The present invention is proposed in view of the above circumstances, and its purpose is to provide an indoor layout support method, system and program that facilitates the study of the placement of goods and the selection of goods to be purchased when goods are placed indoors.

Technical solution

This invention relates to an indoor layout support method, which involves performing the following steps via a computer:

The first step involves acquiring or generating 3D CAD data of the interior space. The second step involves receiving from the user a specification of the category and budget for at least one item among the furniture and home appliances to be placed in the interior space. The third step involves determining, based on the CAD data acquired or generated in the first step and the category and budget specified in the second step, multiple candidate configurations of spaces within the interior space where the item can be placed and the item to be placed in those spaces, displayed in different combinations. The fourth step involves displaying 3D CAD images of the items placed in the spaces where the items can be placed, for each of the multiple candidate configurations determined in the third step. The fifth step involves, when the user specifies an item on the CAD image displayed in the fourth step, displaying a selection screen for other items of the same category as the displayed item; if other items are selected on the selection screen, a placement process is generated. The third step involves having the CAD data for the other goods; and the sixth step involves associating the indoor CAD data of users who have previously purchased goods, the candidate configurations selected by the users, and the goods purchased by the users as historical data stored in memory. In the third step, based on the historical data stored in memory in the sixth step, candidate configurations and goods selected by other users that have indoor CAD data and combinations highly similar to the specified category and its budget are identified as high-priority candidate configurations and goods. In the third step, the candidate configurations and goods are determined using a learning model obtained by performing machine learning on a combination of the indoor CAD data of users who have previously purchased goods, the candidate configurations selected by the users, the goods purchased by the users, and the user's attributes as training data.

This invention executes the following steps via a computer: First, acquiring or generating 3D CAD data of an interior space; second, receiving from a user a specification of the category and budget of at least one item among furniture and home appliances to be placed in the interior space; third, determining, based on the CAD data acquired or generated in the first step and the category and budget of the item specified in the second step, multiple candidate configurations showing spaces in the interior space where the item can be placed and the item to be placed in those spaces in different combinations; fourth, displaying 3D CAD images of the items placed in the spaces where the items can be placed, for each of the multiple candidate configurations determined in the third step; fifth, when the user specifies an item on the CAD image displayed in the fourth step, displaying a selection screen for other items of the same category as the displayed item, and selecting other items on the selection screen... In the case of goods, generate the CAD data containing the other goods; in the sixth step, establish an association between the CAD data of the user's room, the goods purchased by the user, and the price of the purchased goods, and store it in the memory as historical data. In the third step, a learning model obtained by machine learning using the combination of the CAD data of the user's room and the goods purchased by the user as training data is used to determine the candidate configurations and goods, and the following processing is performed: among the determined candidate configurations and goods, according to the historical data stored in the memory in the sixth step, the candidate configurations and goods that have the CAD data of the room, have a high similarity to the combination of the specified category and its budget, and have been selected by other users, are displayed as the candidate configurations and goods with higher priority.

Additionally, the present invention relates to a program that causes a computer to perform the following steps: a first step, acquiring or generating 3D CAD data of an interior; a second step, receiving from a user a specification of the category and budget of at least one item among furniture and home appliances to be placed in the interior; a third step, determining, based on the CAD data acquired or generated in the first step and the category and budget of the item specified in the second step, multiple candidate configurations showing spaces in the interior where the item can be placed and the item to be placed in those spaces in different combinations; a fourth step, displaying 3D CAD images of the items placed in the spaces where the items can be placed, for each of the multiple candidate configurations determined in the third step; and a fifth step, when the user specifies an item on the CAD image displayed in the fourth step, displaying a selection screen for other items of the same category as the displayed item, and selecting items on the selection screen... If other products are selected, CAD data containing those other products is generated; in the sixth step, the indoor CAD data of users who have previously purchased products, the candidate configurations selected by the users, and the products purchased by the users are associated and stored in memory as historical data. In the third step, based on the historical data stored in memory in the sixth step, candidate configurations and products selected by other users that have indoor CAD data and combinations with high similarity to the specified category and budget are identified as high-priority candidate configurations and products. In the third step, the candidate configurations and products are determined by using a learning model obtained by machine learning with the indoor CAD data of users who have previously purchased products, the candidate configurations selected by the users, the products purchased by the users, and the combination of the user's attributes as training data.

Additionally, the present invention relates to a program that causes a computer to perform the following steps: a first step, acquiring or generating 3D CAD data of an interior; a second step, receiving from a user a specification of the category and budget of at least one item among furniture and home appliances to be placed in the interior; a third step, determining, based on the CAD data acquired or generated in the first step and the category and budget of the item specified in the second step, multiple candidate configurations showing spaces in the interior where the item can be placed and the item to be placed in those spaces in different combinations; a fourth step, displaying 3D CAD images of the items placed in the spaces where the items can be placed, for each of the multiple candidate configurations determined in the third step; a fifth step, when the user specifies an item on the CAD image displayed in the fourth step, displaying a selection screen for selecting other items of the same category as the displayed item, and when the selection screen... If other products are selected, the CAD data containing those other products is generated; in the sixth step, the CAD data of the user's room, the products purchased by the user, and the price of the purchased products are associated and stored in the memory as historical data. In the third step, a learning model obtained by using the combination of the CAD data of the user's room and the products purchased by the user as training data is used to determine the candidate configurations and products, and the following processing is performed: among the determined candidate configurations and products, according to the historical data stored in the memory in the sixth step, the candidate configurations and products that have the CAD data of the room, have a high similarity to the combination of the specified category and its budget, and have been selected by other users are displayed as the candidate configurations and products with higher priority.

Invention Effects

According to the present invention, an indoor layout support method, system, and procedure can be provided to facilitate the study of the placement of goods and the selection of goods to be purchased when goods are placed indoors.

Attached Figure Description

Figure 1 is a functional block diagram of a communication terminal device according to an embodiment of the present invention.

Figure 2 is a functional block diagram of a server device according to an embodiment of the present invention.

Figure 3 is a flowchart illustrating the overall process of the indoor layout support method according to the first embodiment of the present invention.

Figure 4 is a flowchart illustrating the process of purchasing goods such as flooring in the indoor layout support method of the first embodiment of the present invention.

Figure 5 is a second variation illustrating the use of the learning completion model to determine candidate configurations and purchase candidate items.

Figure 6 is a diagram illustrating the learning model used in the third variation example.

Figure 7 is a flowchart illustrating the details of the production method for the indoor 3D image shown in Figure 3.

Figure 8 is a flowchart illustrating the overall process of the indoor layout support method according to the second embodiment of the present invention.

Figure 9 is a flowchart illustrating the overall process of the indoor layout support method according to the third embodiment of the present invention.

Detailed Implementation

<First Implementation Method>

The indoor layout support method of this embodiment can be implemented using a communication terminal device 11 such as a smartphone. 3D modeling applications using LiDAR already exist, uniquely capable of measuring in millimeters. The dimensions of the captured room are displayed on the management screen.

In the construction industry, workers often draw up blueprints and manually measure with tape measures when decorating rooms, resulting in high costs. The interior layout support method described in this embodiment can make these tasks more efficient.

In the indoor layout support method of this embodiment, the communication terminal device generates 3D CAD data of the interior by using laser illumination and camera scanning, and determines the size of the room based on the CAD data.

For example, in the indoor layout support method of this embodiment, CAD data is used to display 3D CAD images of the interior.

The finalized interior dimensions are displayed in the 3D CAD image. For example, as shown in Figures 1 to 3, the 3D image displays the height of the ceiling, the length and width of the floor, and other interior dimensions.

Figure 1 is a functional block diagram of a communication terminal device 11 according to an embodiment of the present invention.

As shown in Figure 1, the communication terminal device 11 includes, for example, a laser 50, a camera 51, a display 52, an operation unit 53, a communication unit 55, a Bluetooth (registered trademark) communication unit 57, a memory 59, and a processing unit 61.

As the user moves around the room, the laser 50 illuminates the room with a laser and receives the reflection.

As the user moves around the room, camera 51 captures images of the interior and generates video data.

The measurement results obtained by the laser 50 and the image results obtained by the camera 51 are managed to synchronize them.

The display 51 displays an image based on a signal from the processing unit 61.

The operation unit 53 is an operation unit such as a touch panel, keyboard, and mouse.

The communication unit 55 communicates with the video streaming site server device 11 and the management server device 21.

Bluetooth (registered trademark) communication unit 57 communicates with external devices via Bluetooth (registered trademark).

The memory 59 stores the program PRG1 executed by the processing unit 61.

The processing unit 61 performs the processing of the communication terminal device 11 defined in this embodiment by executing the program PRG1 stored in the memory 59.

Figure 2 is a functional block diagram of a server device 21 according to an embodiment of the present invention.

As shown in FIG2, the server device 21 includes, for example, a communication unit 75, a memory 79, and a processing unit 81.

The memory 79 stores the program PRG2 executed by the processing unit 81.

The processing unit 81 executes the program PRG2 stored in the memory 79 to perform the processing of the server device 21 defined in this embodiment.

The following describes the flow of the indoor layout support method of this embodiment.

<Overall Movement>

Figure 3 is a flowchart illustrating the overall process of the indoor layout support method according to the first embodiment of the present invention.

Each step will be explained.

The processing unit 81 of the server device 21 can perform part or all of the processing performed by the processing unit 61 of the communication terminal device 11 described in this specification.

Step ST11:

Under the control of the processing unit 61, the communication terminal device 11 scans the room by irradiating the room with laser light from the laser 50, and generates 3D image data of the room based on the scan results and the images captured by the camera 51. Then, the processing unit 61 converts the 3D image data into 3D CAD data.

The processing unit 61 displays a 3D CAD image of the interior corresponding to the 3D CAD data on the display 52. For example, the CAD image shown in FIG3 is displayed.

Here, 3D image data is obtained through synthesized mesh data and camera capture results. CAD data is CAD format data that has been completely converted into computer graphics, excluding mesh or photographic data.

Alternatively, the communication terminal device 11 may obtain CAD data from an external source without generating CAD data itself.

At this time, the processing unit 61 determines the areas of the room where point sets have been acquired and the areas where point sets have not been acquired based on the indoor location of the communication terminal device 11 and the images captured by the camera 51. Then, the processing unit 61 displays the areas where point sets have been acquired and the areas where point sets have not been acquired on the screen of the display 52 in different colors. In this way, point sets of all necessary areas of the room can be acquired without omission.

Step ST12:

The user operates the operation unit 53 to specify and select the categories (types) of furniture, home appliances, and other goods to be placed in the room.

For example, the processing unit 61 can display a product category selection bar on the display 52, from which the user can select, or the user can directly enter the product category. This product category could be something like a refrigerator, a shelf, a bookcase, or a sofa.

Step ST13:

Based on the CAD data generated or acquired in step ST11 and the types of goods and budget specified and selected in step ST12, the processing unit 61 determines multiple candidate configurations showing indoor spaces where the goods can be placed and the goods placed in those spaces in different combinations. In each candidate configuration, the goods and the location where they are placed are different from each other. At this time, even if the types of goods are the same, the candidate configurations for placing different goods can be different.

Based on the CAD data in step ST11, the processing unit 61 determines the dimensions of the indoor space where goods can be placed. Specifically, the width, depth, and height are determined.

In addition, the processing unit 61 obtains information from an external server, based on the budget and the dimensions determined above, to determine if there are any products that can be placed in the indoor space. If so, it obtains the data of the product.

For example, the processing unit 61 accesses an external server to retrieve candidate products within the budget based on the product category specified in step ST12, and obtains the dimensions of the candidate products.

Next, the processing unit 61 determines a space within the indoor space where the candidate product of the aforementioned dimensions can be placed. If multiple such spaces exist, a candidate configuration is determined for each space.

If multiple product categories are specified, the available space is determined for each product. In this case, candidate configurations are determined in a way that prevents products from different categories from being placed in the same space.

Step ST14:

The processing unit 61 performs processing to display 3D CAD images of the goods after they are placed in the space where the goods can be placed, for each of the multiple candidate configurations determined in step ST13.

While observing CAD images of multiple candidate configurations, users study which pattern (the combination of goods and their placement) is best and then perform the appropriate action.

Furthermore, when a user operates the operation unit 53 to specify a product on the CAD image, the processing unit 61 displays a selection screen on the display 52 to select other products of the same category as the displayed product.

When another product is selected on the selection screen, the processing unit 61 generates CAD data after the other product is placed, and displays a CAD image based on the CAD data.

Step ST15:

When a user operates the operation unit 53 to request the purchase of the item selected on the CAD image displayed in step ST14, the processing unit 61 proceeds to step ST16.

Step ST16:

When a purchase request is entered in step ST15, the processing unit 61 performs purchase processing on the product selected by the user.

The communication terminal device 11 accesses the server device 21 or other product sales websites via the communication unit 55 to perform purchase processing.

<Purchase of flooring and other products>

Figure 4 is a flowchart illustrating the process of purchasing goods such as flooring in the indoor layout support method of the first embodiment of the present invention.

Each step will be explained.

Step ST21:

Based on the CAD data generated in step ST11 shown in Figure 3, the user operates the operation unit 53 to specify the operation of items such as interior walls or floors on the interior CAD image displayed on the monitor 52.

Step ST22:

The processing unit 61 determines the category (type) of the goods to be operated in step ST21 and obtains supplier information showing the suppliers that provide goods that have been pre-associated with that category.

Then, based on the acquired supplier information, the processing unit 61 provides the CAD data corresponding to the CAD image to be operated to the supplier's communication device.

The CAD data can be provided by sending it to the supplier's email address or by uploading it to a system accessible to the supplier.

Step ST23:

Based on the CAD data obtained in step ST22, the supplier sends the product data (candidate product data) of multiple candidate products to the communication terminal device 11.

The processing unit 61 stores multiple candidate product data received from the supplier in the memory 59.

In addition, the communication terminal device 11 can obtain product data, the price of the product, and quotation information of construction costs from the supplier, and display the information on the display 52.

Step ST24:

The processing unit 61 generates new CAD data based on the indoor CAD data and the candidate product data stored in the memory 59 in step ST23, after loading the image corresponding to the candidate product data into the product position, and generates a CAD image corresponding to the CAD data.

The CAD image is generated for each of the multiple candidate product data.

Step ST25:

When a user operates the operation unit 53 to request the purchase of the item selected on the CAD image displayed in step ST23, the processing unit 61 proceeds to step ST25.

Step ST26:

When a purchase request is entered in step ST24, the processing unit 61 performs purchase processing on the product selected by the user.

<First Variation of the First Embodiment>

After the processing described in the first embodiment, the processing unit 61 associates the indoor CAD data of users who have previously purchased goods, the candidate configuration selected by the user, and the goods purchased by the user, and stores them as historical data in the memory 59.

When multiple candidate CAD images are displayed on the display 52 in step ST14 as shown in Figure 3, the processing unit 61 determines the candidate configurations and products with high priority based on the historical data stored in the memory 59, which have a high similarity to the combination of indoor CAD data generated or acquired in step ST11 and the combination of the product category and budget specified in step ST12.

High similarity means, for example, the more commonalities there are in the area and shape of interior spaces based on CAD data, the more commonalities there are in the specified product categories, or the closer the budgets are, the higher the similarity is.

Then, in step ST13, candidate configurations and products with high similarity are displayed on display 52 with higher priority as CAD images.

High-priority display means that priority can be represented by numbers or other methods, or that items displayed on the screen in descending order of priority will be displayed earlier.

This allows the system to prioritize displaying candidate configurations and products that users are more likely to choose, enabling users to make decisions efficiently.

<Second Variation of the First Embodiment>

In step ST13 shown in Figure 3, the processing unit 61 can use the learned model to determine candidate configurations and purchase candidate products.

Figure 5 is a second variation illustrating the use of the learning completion model to determine candidate configurations and purchase candidate items.

That is, the processing unit 61 uses the learned model to perform the processing of step ST13 shown in Figure 3.

The learning model was obtained by using a combination of indoor CAD data of users who had previously purchased goods, candidate configurations selected by the users, the goods purchased by the users, and the user's attributes as training data for machine learning.

The processing unit 61 can create a completed learning model, or it can relearn based on newly acquired information. Alternatively, the completed learning model can be generated by other systems.

The processing unit 61 inputs CAD data and the user's attributes into the learning model and outputs information on candidate configurations and candidate products to purchase. This output can choose to show only the highest priority information or set a priority order for outputting multiple pieces of information.

In step ST14, the processing unit 61 generates candidate CAD images based on the output of the learning model and the CAD data.

In this way, by using the learning-complete model, as the amount of training data increases, it is possible to determine candidates such as attributes suitable for users.

This second variation can also be combined with the functions of the first variation described above.

By employing a learning model in this way, more suitable candidate configurations and purchase options can be shown to users as actual results increase.

<Third variation of the first embodiment>

In the third variation, after the processing of the above embodiment 1, the processing unit 61 establishes an association between the indoor CAD data of users who have previously purchased goods, the goods purchased by the users, and the price of the purchased goods, and stores them as historical data in the memory 59.

Furthermore, in step ST13 shown in Figure 3, the processing unit 61 uses the CAD data of the interior of a user who has previously purchased goods and the combination of goods purchased by that user as training data to perform machine learning and determine candidate configurations and goods.

By employing a learning model in this way, more suitable candidate configurations and purchase options can be displayed to users as actual results increase.

Figure 6 is a diagram illustrating the learning model used in the third variation example.

As shown in Figure 6, in this learning model, the user's indoor CAD data and the product data specified by the user are input, and the configuration candidates and purchase candidates of the products are output.

Next, the processing unit 61 performs the following processing: based on the historical data stored in the memory 59 in step ST16 shown in FIG3, among the determined candidate configurations and products, the candidate configurations and products that have indoor CAD data, have a high similarity to the combination of the user-specified category and budget, and have been selected by other users are identified as high priority candidate configurations and products and are displayed.

High similarity means, for example, the more commonalities there are in the area and shape of interior spaces based on CAD data, the more commonalities there are in the specified product categories and budgets (purchase prices), or the closer the budgets are, the higher the similarity.

The processing unit 61 prioritizes displaying candidate configurations and products with high similarity as CAD images on the display 52.

High-priority display means that priority can be represented by numbers or other symbols, or that items displayed on the screen in descending order of priority will be displayed earlier.

This allows the system to prioritize displaying candidate configurations and products that users are more likely to choose, enabling users to make decisions efficiently.

Note that, apart from the content described as this third variation, the third variation is the same as the content described in the first embodiment, etc.

According to the third variation, in addition to using a learning model to determine candidate configurations and products more suitable for the user's situation, candidate configurations and products selected by the user that have indoor CAD data and high similarity to the specified category and budget combination are also identified as "high-priority" candidate configurations and products. That is, the determination of candidates and the determination of high-priority candidates are carried out in two stages. As a result, users can more easily select candidate configurations and products that suit them.

The following describes the method for generating indoor 3D CAD data using the communication terminal device 11 in step ST11 shown in Figure 3.

Figure 7 is a flowchart illustrating the details of the method for generating the indoor 3D image shown in Figure 3.

Each step in Figure 4 will be explained below.

Step ST41:

The processing unit 61 of the communication terminal device 11 identifies (infers) the location of the communication terminal device 11 indoors. For self-location inference, for example, a LiDAR-based self-location inference technique is used.

Step ST42:

The user moves the communication terminal device 11 to irradiate a laser from the laser 50 onto the entire area or a portion of the room and detects the reflection. Then, the processing unit 61 generates point set data of the room and objects within it based on the detection results.

At the same time, camera 51 captures images of the laser irradiation position illuminated by laser 50.

Step ST43:

The processing unit 61 converts the point set data generated in step ST42 from the coordinate system of the location of the communication terminal device 11 determined in step ST41 to the indoor coordinate system.

Step ST44:

The processing unit 61 extracts valid data (deletes unnecessary data) from the point set data transformed in step ST43. This extraction process is performed by deleting point sets that have the same or similar attributes as point sets that are predetermined to be unnecessary.

Step ST45:

The processing unit 61 converts the point set data after the extraction process in step ST44 into grid (polygon) data.

Step ST46:

The processing unit 61 converts the mesh data generated in step ST45 into surface (face) data. This conversion uses a predetermined planar detection technique.

Step ST47:

The processing unit 61 generates 3D image data (3D image) by pasting the image captured by the camera 51 obtained in step ST42 onto the grid data based on the surface data generated in step ST46.

Step ST48:

The processing unit 61 converts the 3D image data generated in step ST47 into 3D CAD data and displays it on the display 52.

The processing unit 61 determines (measures) the interior dimensions (height, length, width, layout) based on the 3D CAD data and displays the dimensions on the 3D CAD image.

Furthermore, through the above processing, CAD data can be generated as long as the walls are identified, thus enabling the generation of 3D images of layouts without interior furniture, etc.

It should be noted that a learning model can be used in some of the steps of the above processing.

According to the indoor layout support method of this embodiment, 3D conversion can be performed instantly, easily and with high precision anywhere by using a communication terminal device 11 such as a smartphone, thus contributing to the application or widespread adoption of 3D technology in all fields.

For example, it can be used in real estate businesses such as the decoration industry, construction companies, and moving companies.

Furthermore, the interior layout support method according to this embodiment can improve the labor shortage in the construction industry and the experience during decoration.

In other words, if 3D scanners can replace the previously manual measurement and drawing, human resources can be allocated to other higher-level businesses, thereby providing higher value-added products, which will also reduce costs.

Furthermore, it creates an environment where staff can focus on their assigned tasks, and the previously experiential learning of craftsmanship becomes simpler and clearer to some extent. It also contributes to the onboarding and training of young talent who will take on future responsibilities.

Measurements can be completed very easily and quickly by using 3D scanning technology.

Furthermore, by adding color to the 3D image, inserting photos, and viewing it from the front, back, left, and right in 360 degrees, it eliminates the image that only exists in each person's mind, allowing each relevant person to conduct business with the same generated image.

Because it can be managed on the equipment, paper waste can also be reduced. These efficiency improvements will ultimately benefit consumers, enabling them to decorate their buildings at a more economical cost than before.

Furthermore, according to this embodiment, an application has been developed that can perform high-precision, high-quality measurements and create 3D models using only a smartphone (communication terminal device 11) equipped with LiDAR, allowing anyone to easily and inexpensively perform 3D scanning.

According to this embodiment, measurable 3D model and photo data can be acquired and stored simply by using the communication terminal device 11 to capture indoor video.

Furthermore, according to this embodiment, it can be output and shared to a personal computer in various formats such as point sets or grids.

Furthermore, according to the communication terminal device 11, through the processing shown in Figure 4, measurements with higher precision than those made with a measuring tape can be performed.

Furthermore, according to this embodiment, multiple candidate configurations of different modes can be provided to users based on the type of goods they wish to purchase and their budget, and candidate configurations that users would not have thought of can be included in the research object.

Because multiple candidate configurations and candidate products can be determined at the same time, and the user is provided with a CAD image of the product placed in each candidate configuration, the user can have a more vivid impression of the product being placed in each candidate configuration based on the CAD image, avoiding the gap between impression and reality.

Furthermore, according to this embodiment, users only need to specify products such as flooring on the CAD image, and based on product data from the product supplier, they can easily grasp the indoor impression when using the product from the CAD image.

<Second Implementation Method>

Figure 8 is a flowchart illustrating the overall process of the indoor layout support method according to the second embodiment of the present invention.

Each step will be explained.

The processing unit 81 of the server device 21 can perform part or all of the processing performed by the processing unit 61 of the communication terminal device 11 described in this specification.

Step ST51:

Under the control of the processing unit 61, the communication terminal device 11 scans the room by irradiating the room with laser light from the laser 50, and generates 3D image data of the room based on the scan results and the images captured by the camera 51. Then, the processing unit 61 converts the 3D image data into 3D CAD data.

The processing unit 61 displays a 3D CAD image of the interior corresponding to the 3D CAD data on the display 52. For example, the CAD image shown in FIG3 is displayed.

Here, 3D image data is obtained through synthesized mesh data and camera capture results. CAD data is CAD format data that has been fully converted into computer graphics, excluding mesh and photographic data.

Alternatively, the communication terminal device 11 may obtain CAD data from an external source without generating CAD data itself.

At this time, the processing unit 61 determines the areas of the room where point sets have been acquired and the areas where point sets have not been acquired based on the indoor location of the communication terminal device 11 and the images captured by the camera 51. Then, the processing unit 61 displays the areas where point sets have been acquired and the areas where point sets have not been acquired on the screen of the display 52 in different colors. In this way, point sets of all necessary areas of the room can be obtained.

Step ST52:

On the CAD image displayed in step ST51, the user operates the operation unit 53 to specify the space where the goods will be placed indoors. This space is specified by moving the cursor to enclose an area, etc.

Step ST53:

The user operates the operation unit 53 to input the product category of the goods to be placed in the space specified in step ST52. This input can be done by displaying a product category selection bar on the display 52 and allowing the user to select from it, or the user can directly input the product category. The product category includes items such as refrigerators, shelves, bookshelves, and sofas.

Step ST54:

Based on the CAD data in step ST51, the processing unit 61 of the communication terminal device 11 determines the dimensions of the space specified in step ST52. Specifically, the width, depth, height, etc., are determined.

Step ST55:

Processing unit 61 retrieves items from the product categories entered in step ST53 that can be placed in the space of the size specified in step ST54. In this retrieval, the user can specify at least one of budget, product brand, and features, and can retrieve products that match these specifications.

The processing unit 61 displays a product selection screen showing the search results on the display 52.

Users use the product selection screen to select products.

The communication terminal device 11 accesses the server device 21 or other product sales websites and displays the search results.

Step ST56:

The processing unit 61 displays a 3D CAD image on the display 52 after the product selected in step ST55 is placed in the space specified in step ST52.

Step ST57:

When a user operates the operation unit 53 to request to purchase the product selected in step ST55, the processing unit 61 proceeds to step ST58.

When a purchase request is entered in step ST57, the processing unit 61 performs purchase processing on the product selected by the user.

The communication terminal device 11 accesses the server device 21 or other product sales websites via the communication unit 55 to perform purchase processing.

<Third Implementation Method>

In the above embodiments, the case of purchasing goods to be placed in the scanned room was illustrated. In this embodiment, the case of updating goods (materials) such as walls or floors in the research room will be described.

The following describes the flow of the indoor layout support method of this embodiment.

Figure 9 is a flowchart illustrating the overall process of the indoor layout support method according to the third embodiment of the present invention.

Each step will be explained.

Step ST61:

Under the control of the processing unit 61, the communication terminal device 11 scans the room by irradiating the room with laser light from the laser 50, and generates 3D image data of the room based on the scanning results and the images captured by the camera 51. Then, the processing unit 61 converts the 3D image data into 3D CAD data.

Step ST61 is the same as step ST11 described in the first embodiment.

Step ST62:

The user operates the operation unit 53 to specify items (building materials) such as indoor floors and walls on the CAD image displayed in step ST61. This specification is performed by clicking the image of the specified item with the cursor, or by moving the cursor to surround the area.

Step ST63:

The user operates the operation unit 53 to input the product category specified in step ST62. This input can be made by displaying a product category selection bar on the display 52 and allowing the user to select from it, or the user can directly input the product category. The product category could be, for example, walls, flooring, or similar items.

Step ST64:

The processing unit 61 of the communication terminal device 11 sends (shares) the product and 3D image specified by the user in step ST61 to a supplier (design, decoration, construction, etc.) that has been pre-associated with the product category specified by the user in step ST63. At this time, the communication terminal device 11 sends the product's dimensions (wall dimensions, floor dimensions, etc.) to the supplier's terminal device. Furthermore, information such as the user's requirements for the product can be sent from the communication terminal device 11 to the supplier's terminal device.

Step ST65:

Based on the products, user requirements, and CAD images shared in step ST64, the supplier suggests suitable indoor products for the user and sends quotation information such as product prices and construction costs to the communication terminal device 11. At this time, the supplier can provide the communication terminal device 11 with CAD images that incorporate images of the products. Furthermore, suggestion information regarding the products can be sent from the supplier's terminal device to the communication terminal device 11.

As described above, according to the indoor layout support method of the second embodiment, even when it comes to products such as indoor building materials, users can request suppliers to provide suggestions and quotations simply by specifying the products from the indoor CAD image using the communication terminal device 11.

Furthermore, since suppliers can obtain 3D images of the user's interior, they can offer more reasonable suggestions and quotes, including renovations.

Furthermore, since suppliers such as design firms can utilize the functions of the communication terminal device 11, they are able to offer more reasonable suggestions.

The present invention is not limited to the embodiments described above.

That is, those skilled in the art can make various changes, combinations, sub-combinations and substitutions to the constituent elements of the above embodiments within the technical scope of the present invention or its equivalents.

Industrial applicability

This invention can be applied to systems for selecting goods placed indoors.

(Explanation of reference numerals in the attached diagram)

11...Communication terminal device

21...Server Device

50... laser

51...camera

52...monitor

53... Operations Department

55...Ministry of Communications

57...Bluetooth (registered trademark) Communications Department

61... Processing Department

75...Ministry of Communications

79... Memory

Claims (12)

1. An indoor layout support method, comprising the following steps performed by a computer: The first step is to acquire or generate 3D CAD data for the interior. The second step involves receiving from the user a specification of the category and budget for at least one item among the furniture and home appliances to be placed in the room; The third step involves determining, based on the CAD data acquired or generated in the first step and the product category and budget specified in the second step, multiple candidate configurations of space in the interior where the products can be placed and the products to be placed in the space, showing different combinations of these configurations. The fourth step involves displaying 3D CAD images of the goods placed in the available spaces for each of the multiple candidate configurations determined in the third step. The fifth step is to display a selection screen for other products of the same category as the displayed product when the user specifies a product on the CAD image shown in the fourth step. If other products are selected on the selection screen, the CAD data containing the other products is generated. The sixth step involves establishing a correlation between the indoor CAD data of users who have previously purchased goods, the goods purchased by those users, and the prices of those goods, and storing this data as historical data in the memory. In the third step, The candidate configurations and products are determined by using a machine learning model obtained through training data that combines the indoor CAD data of users who have previously purchased goods with the goods purchased by those users. The following processing is performed: Among the determined candidate configurations and products, based on the historical data stored in the memory in the sixth step, candidate configurations and products that have a high similarity to the combination of the indoor CAD data, the specified category and its budget, and have been selected by other users, are displayed as candidate configurations and products with higher priority. 2. The indoor layout support method according to claim 1, wherein the following steps are further performed by the computer: The seventh step is to receive from the user a designation of at least one item from the floor and walls of the interior, based on the CAD data. The eighth step involves transmitting the CAD data to a supplier who has been pre-associated with the goods specified in the seventh step. The ninth step involves receiving candidate product data from the supplier regarding the product specified in the seventh step. In the fourth step, the CAD image is displayed based on the product data received in the ninth step. 3. The indoor layout support method according to claim 1, wherein, In the second step, the user's family structure is specified from the user. In the third step, a variety of candidate configurations are determined based on the family structure specified in the second step. 4. The indoor layout support method according to claim 1, wherein, After the fourth step, the method further includes a step of processing the user to purchase the purchase candidate according to the instruction from the user. 5. The indoor layout support method according to claim 1, wherein the first step involves the following steps being performed by the computer: Step 11: Determine the indoor location of the main equipment for laser irradiation; The twelfth step is to generate a point set of the interior and its objects by irradiating them with the laser. The thirteenth step is to convert the generated point set from the coordinate system of the determined main equipment to the indoor coordinate system; The fourteenth step is to extract valid data from the set of points. The fifteenth step is to convert the point set into grid (polygon) data; The sixteenth step is to convert the mesh data into surface data; The seventeenth step is to paste the captured image onto the grid data based on the surface data to generate 3D image data. The eighteenth step is to convert the 3D image data generated in the seventeenth step into 3D CAD data. 6. The indoor layout support method according to claim 5, wherein, Based on the surface data, the shape and size of the interior are determined, and the dimensions are displayed in the display image. 7. The indoor layout support method according to claim 5, wherein, Based on the location of the main device and the captured image, the regions in the room where the point set has been acquired and the regions where the point set has not been acquired are determined, and the regions where the point set has been acquired and the regions where the point set has not been acquired are displayed on the screen in different colors. 8. An interior layout support method, comprising the following steps performed by a computer: The first step is to acquire or generate 3D CAD data for the interior. The second step involves receiving from the user a specification of the category and budget for at least one item among the furniture and home appliances to be placed in the room; The third step involves determining, based on the CAD data acquired or generated in the first step and the product category and budget specified in the second step, multiple candidate configurations of space in the interior where the products can be placed and the products to be placed in the space, showing different combinations of these configurations. The fourth step involves displaying 3D CAD images of the goods placed in the available spaces for each of the multiple candidate configurations determined in the third step. The fifth step is to display a selection screen for other products of the same category as the displayed product when the user specifies a product on the CAD image shown in the fourth step. If other products are selected on the selection screen, the CAD data containing those other products is generated. The sixth step involves associating the indoor CAD data of users who have previously purchased goods, the candidate configurations selected by those users, and the goods purchased by those users with historical data, storing this information in the memory. In the third step, based on the historical data stored in the memory in the sixth step, candidate configurations and products that have indoor CAD data, high similarity to the specified category and its budget, and have been selected by other users are identified as high-priority candidate configurations and products. In the third step, a learning model obtained by machine learning, which uses a combination of the CAD data of the user's interior, the candidate configuration selected by the user, the product purchased by the user, and the user's attributes as training data, is used to determine the candidate configuration and product. 9. A program that causes a computer to perform the following steps: The first step is to acquire or generate 3D CAD data for the interior. The second step involves receiving from the user a specification of the category and budget for at least one item among the furniture and home appliances to be placed in the room; The third step involves determining, based on the CAD data acquired or generated in the first step and the product category and budget specified in the second step, multiple candidate configurations of space in the interior where the products can be placed and the products to be placed in the space, showing different combinations of these configurations. The fourth step involves displaying 3D CAD images of the goods placed in the available spaces for each of the multiple candidate configurations determined in the third step. The fifth step is to display a selection screen for other products of the same category as the displayed product when the user specifies a product on the CAD image shown in the fourth step. If other products are selected on the selection screen, the CAD data containing the other products is generated. The sixth step involves establishing a correlation between the indoor CAD data of users who have previously purchased goods, the goods purchased by those users, and the prices of those goods, and storing this data as historical data in the memory. In the third step, The candidate configurations and products are determined by using a machine learning model obtained through training data that combines the indoor CAD data of users who have previously purchased goods with the goods purchased by those users. The following processing is performed: Among the determined candidate configurations and products, based on the historical data stored in the memory in the sixth step, candidate configurations and products that have indoor CAD data, combinations with high similarity to the specified category and its budget, and have been selected by other users are displayed as candidate configurations and products with high priority. 10. A program that causes a computer to perform the following steps: The first step is to acquire or generate 3D CAD data for the interior. The second step involves receiving from the user a specification of the category and budget for at least one item among the furniture and home appliances to be placed in the room; The third step involves determining, based on the CAD data acquired or generated in the first step and the product category and budget specified in the second step, multiple candidate configurations of space in the interior where the products can be placed and the products to be placed in the space, showing different combinations of these configurations. The fourth step involves displaying 3D CAD images of the goods placed in the available spaces for each of the multiple candidate configurations determined in the third step. The fifth step is to display a selection screen for other products of the same category as the displayed product when the user specifies a product on the CAD image shown in the fourth step. If other products are selected on the selection screen, the CAD data containing the other products is generated. The sixth step involves associating the indoor CAD data of users who have previously purchased goods, the candidate configurations selected by those users, and the goods purchased by those users with historical data, storing this information in the memory. In the third step, based on the historical data stored in the memory in the sixth step, candidate configurations and products that have been selected by other users and have a high similarity to the indoor CAD data and the specified category and budget are identified as high-priority candidate configurations and products. In the third step, a learning model obtained by machine learning is used, which uses a combination of the CAD data of the user's interior, the candidate configuration selected by the user, the product purchased by the user, and the user's attributes as training data, to determine the candidate configuration and product. 11. An interior layout support system, comprising: The first unit acquires or generates 3D CAD data for the interior. The second unit receives from the user a specification of the category and budget for at least one item among the furniture and household appliances to be placed in the room; The third unit, based on the CAD data acquired or generated in the first unit and the product category and budget specified in the second unit, determines multiple candidate configurations of space in the interior where the products can be placed and the products to be placed in the space, showing different combinations of these configurations. The fourth unit displays 3D CAD images of the goods placed in the available spaces for each of the multiple candidate configurations determined in the third unit. The fifth unit, when the user specifies a product on the CAD image displayed in the fourth unit, displays a selection screen for other products of the same category as the displayed product. When other products are selected on the selection screen, it generates the CAD data containing the other products. The sixth unit establishes a correlation between the indoor CAD data of users who have previously purchased goods, the goods purchased by the users, and the prices of those goods, storing this data as historical data in the memory. In the third unit, The candidate configurations and products are determined by using a machine learning model obtained through training data that combines the indoor CAD data of users who have previously purchased goods with the goods purchased by those users. The following processing is performed: Among the determined candidate configurations and products, based on the historical data stored in the memory in the sixth unit, candidate configurations and products that have indoor CAD data, combinations with high similarity to the specified category and its budget, and have been selected by other users are displayed as candidate configurations and products with high priority. 12. An interior layout support system, comprising: The first unit acquires or generates 3D CAD data for the interior. The second unit receives from the user a specification of the category and budget for at least one item among the furniture and household appliances to be placed in the room; The third unit, based on the CAD data acquired or generated in the first unit and the product category and budget specified in the second unit, determines multiple candidate configurations of space in the interior where the products can be placed and products to be placed in the space, showing different combinations of these configurations. The fourth unit displays 3D CAD images of the goods placed in the available spaces for each of the multiple candidate configurations determined in the third unit. The fifth unit, when the user specifies a product on the CAD image displayed in the fourth unit, displays a selection screen for other products of the same category as the displayed product. When other products are selected on the selection screen, it generates the CAD data containing the other products. The sixth unit establishes a correlation between the indoor CAD data of users who have previously purchased goods, the candidate configurations selected by the users, and the goods purchased by the users, and stores this data as historical data in the memory. The third unit, based on the historical data stored in the memory of the sixth unit, identifies candidate configurations and products that have been selected by other users and have indoor CAD data, combinations with high similarity to the specified category and its budget, as high-priority candidate configurations and products. The third unit uses a learning model obtained by performing machine learning on a combination of the indoor CAD data of users who have previously purchased goods, the candidate configurations selected by the users, the goods purchased by the users, and the user's attributes as training data to determine the candidate configurations and goods.