Indoor layout support method, system, and program

Abstract

The invention provides a convenient indoor layout support method so as to research the indoor layout of commodities. The method comprises the following steps of: 1, acquiring or generating indoor 3D CAD (Computer Aided Design) data; a second step in which the user specifies a category of at least one of furniture and household appliances to be placed in the room and a budget thereof; a third step of determining a plurality of candidate arrangements showing, in different combinations, a space in which a product can be placed and a product to be placed in the space in the room, on the basis of the CAD data acquired or generated in the first step and the category and budget of the product specified in the second step; and a fourth step of displaying, for each of the plurality of candidate arrangements determined in the third step, a 3D CAD image in which the product is placed in the space in which the product can be placed.

Description

Technical Field

[0001] The present invention relates to an indoor layout support method, system and program for studying the layout of goods in an indoor space. Background Art

[0002] For example, in real estate-related businesses such as construction, renovation, and moving, which require high safety and accuracy, drawing drawings such as design drawings and site plans is one of the necessary responsibilities. Conventionally, these are made by architects and experts who can use CAD.

[0003] In this case, the site is first measured, a reduced-scale floor plan is drawn up based on the measurement results, and special symbols, values, materials, colors, etc. are written on the floor plan. This work alone takes about one to two weeks.

[0004] Prior art literature

[0005] Patent Literature

[0006] Patent Document 1: Japanese Patent Application Publication No. 2021-012528 Summary of the invention

[0007] Problem to be solved

[0008] The drawings and instructions produced in this way become three-dimensional impressions in the minds of those who see them, and construction, wiring, object setting, coloring, etc. are carried out according to the drawn dimensions.

[0009] Since there are many people involved in the construction or renovation of a building, in order to ensure that the three-dimensional impression in the minds of each person is not distorted, they all work carefully while checking with each other.

[0010] In addition, each practitioner carries dozens of drawn paper drawings and instructions when working, so a large amount of paper is required.

[0011] Therefore, traditional construction sites have the problem of a large waste of "manpower", "time" and "resources".

[0012] There is also a demand for efficiently selecting and purchasing home appliances, furniture and other products based on interior CAD data.

[0013] The present invention is proposed in view of the above situation, and its purpose is to provide an indoor layout support method, system and program that facilitates studying the placement of goods and selecting goods to be purchased when placing goods indoors.

[0014] Technical Solution

[0015] The present invention relates to an indoor layout support method, which comprises executing the following steps by a computer:

[0016] The first step is to obtain or generate 3D CAD data of a room; the second step is to receive from a user a designation of a category and a budget of at least one commodity among furniture and household appliances to be placed in the room; the third step is to determine, based on the CAD data obtained or generated in the first step and the category and budget of the commodity specified in the second step, a plurality of candidate configurations showing spaces in the room where the commodity can be placed and commodities to be placed in the spaces in different combinations; the fourth step is to display, for each of the plurality of candidate configurations determined in the third step, 3D images of the commodity placed in the spaces where the commodity can be placed. CAD image; a fifth step, when the user specifies a product on the CAD image displayed in the fourth step, a selection screen for selecting other products of the same category as the displayed product is displayed, and when other products are selected on the selection screen, the CAD data on which the other products are placed is generated; and a sixth step, establishing an association between the CAD data of the interior of a user who has purchased products in the past, the candidate configuration selected by the user, and the products purchased by the user, and storing them in a memory as historical data, wherein in the third step, based on the historical data stored in the memory in the sixth step, the candidate configurations and products selected by other users that have a high degree of similarity with the combination of the CAD data of the interior and the specified category and its budget are determined as the candidate configurations and products with high priority, and in the third step, the candidate configurations and products are determined using a learning model obtained by machine learning using a combination of the CAD data of the interior of a user who has purchased products in the past, the candidate configuration selected by the user, the products purchased by the user, and the attributes of the user as training data.

[0017] The present invention executes the following steps by a computer: a first step of acquiring or generating 3D CAD data of a room; a second step of receiving from a user a designation of a category and a budget of at least one commodity among furniture and household appliances to be placed in the room; a third step of determining, based on the CAD data acquired or generated in the first step and the category and budget of the commodity specified in the second step, a plurality of candidate configurations showing spaces in the room where the commodity can be placed and commodities to be placed in the spaces in different combinations; a fourth step of displaying, for the plurality of candidate configurations determined in the third step, 3D images of the commodity placed in the spaces where the commodity can be placed. CAD image; a fifth step, when the user specifies a product on the CAD image displayed in the fourth step, a selection screen for selecting other products of the same category as the displayed product is displayed, and when other products are selected on the selection screen, the CAD data on which the other products are placed is generated; a sixth step, establishing an association between the CAD data of the interior of a user who has purchased products in the past, the products purchased by the user, and the prices of the purchased products, and storing them in a memory as historical data, wherein in the third step, a learning model obtained by machine learning using a combination of the CAD data of the interior of a user who has purchased products in the past 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, the candidate configurations and products having the CAD data of the interior, a combination with a high degree of similarity to the specified category and its budget, and selected by other users are displayed as the candidate configurations and products with high priority according to the historical data stored in the memory in the sixth step.

[0018] In addition, the present invention relates to a program, which causes a computer to execute the following steps: a first step of acquiring or generating 3D CAD data of a room; a second step of receiving, from a user, a designation of a category and a budget of at least one commodity among furniture and household appliances to be placed in the room; a third step of determining, based on the CAD data acquired or generated in the first step and the category and budget of the commodity specified in the second step, a plurality of candidate configurations showing spaces in the room where the commodity can be placed and commodities to be placed in the spaces in different combinations; a fourth step of respectively displaying, for each of the plurality of candidate configurations determined in the third step, 3D images of the commodity placed in the spaces where the commodity can be placed. CAD image; a fifth step, when the user specifies a product on the CAD image displayed in the fourth step, a selection screen for selecting other products of the same category as the displayed product is displayed, and when other products are selected on the selection screen, the CAD data on which the other products are placed is generated; a sixth step, establishing an association between the CAD data of the interior of a user who has purchased products in the past, the candidate configuration selected by the user, and the products purchased by the user, and storing them in a memory as historical data, wherein in the third step, based on the historical data stored in the memory in the sixth step, the candidate configurations and products selected by other users that have a high degree of similarity with the combination of the CAD data of the interior and the specified category and its budget are determined as the candidate configurations and products with high priority, and in the third step, the candidate configurations and products are determined using a learning model obtained by machine learning using a combination of the CAD data of the interior of a user who has purchased products in the past, the candidate configuration selected by the user, the products purchased by the user, and the attributes of the user as training data.

[0019] In addition, the present invention relates to a program, which causes a computer to execute the following steps: a first step of acquiring or generating 3D CAD data of a room; a second step of receiving, from a user, a designation of a category of at least one product among furniture and household appliances to be placed in the room and a budget thereof; a third step of determining, based on the CAD data acquired or generated in the first step and the category and budget of the product specified in the second step, a plurality of candidate configurations showing in different combinations a space in the room where the product can be placed and the product to be placed in the space; a fourth step of displaying, for each of the plurality of candidate configurations determined in the third step, 3D CAD images in which the product is placed in the space where the product can be placed; and a fifth step of displaying, when the user specifies a product on the CAD image displayed in the fourth step, a selection screen for selecting other products of the same category as the displayed product, and generating, when the other product is selected on the selection screen, a 3D CAD image in which the other product is placed. the CAD data of the product; in the sixth step, the CAD data of the interior of the user who has purchased the product in the past, the product purchased by the user and the price of the purchased product are associated, and stored in the memory as historical data, wherein in the third step, the candidate configurations and products are determined by using a learning model obtained by machine learning using the combination of the CAD data of the interior of the user who has purchased the product in the past and the product purchased by the user as training data, and the following processing is performed: among the determined candidate configurations and products, the candidate configurations and products that have the CAD data of the interior, a combination with a high degree of similarity to the specified category and its budget, and have been selected by other users are displayed as the candidate configurations and products with high priority according to the historical data stored in the memory in the sixth step.

[0020] Effects of the Invention

[0021] According to the present invention, an indoor layout support method, system and program can be provided for facilitating research on locations for placing goods and selection of goods to be purchased when placing goods indoors. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a functional block diagram of a communication terminal device according to an embodiment of the present invention.

[0023] Figure 2 is a functional block diagram of a server device according to an embodiment of the present invention.

[0024] Figure 3 This is a flowchart for explaining the overall flow of the indoor layout support method according to the first embodiment of the present invention.

[0025] Figure 4This is a flowchart for explaining the flow of a purchase operation of a commodity such as a floor covering in the interior layout support method according to the first embodiment of the present invention.

[0026] Figure 5 It is a diagram for explaining a second variation example of determining candidate configurations and candidate purchase products using a learned model.

[0027] Figure 6 This is a diagram for explaining the learning model used in the third variation.

[0028] Figure 7 It is used to illustrate Figure 3 Flowchart showing details of the method for producing a 3D image of a room.

[0029] Figure 8 This is a flowchart for explaining the overall flow of the indoor layout support method according to the second embodiment of the present invention.

[0030] Fig. 9 This is a flowchart for explaining the overall flow of the indoor layout support method according to the third embodiment of the present invention. DETAILED DESCRIPTION

[0031] <First Embodiment>

[0032] The indoor layout support method of this embodiment can be implemented using a communication terminal device 11 such as a smartphone. There are already 3D modeling applications using laser radar, which are unique in that they can measure in millimeters. The dimensions of the captured room are displayed on the management screen.

[0033] When decorating a room, etc., workers in the construction industry draw blueprints and use tape measures to measure manually, which results in high costs. The indoor layout support method of this embodiment can make these tasks more efficient.

[0034] In the indoor layout support method of the present embodiment, the communication terminal device generates indoor 3D CAD data by scanning the indoor space with laser irradiation and a camera, and determines the size of the room based on the CAD data.

[0035] For example, in the interior layout support method of the present embodiment, a 3D CAD image of the interior is displayed based on CAD data.

[0036] The determined interior dimensions are displayed in the 3D CAD image. Figures 1 to 3 As shown, the height of the indoor ceiling, the length and width of the ground, etc. are displayed in the 3D image.

[0037] Figure 1 1 is a functional block diagram of the communication terminal device 11 according to the embodiment of the present invention.

[0038] like Figure 1 As shown, 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 .

[0039] As the user moves in the room, the laser 50 illuminates the room with laser light and receives its reflection.

[0040] As the user moves in the room, the camera 51 captures the interior of the room and generates video data.

[0041] The measurement results obtained by the laser 50 and the image results obtained by the camera 51 are managed so as to be synchronized.

[0042] The display 51 displays an image based on a signal from the processing unit 61 .

[0043] The operation unit 53 is an operation unit such as a touch panel, a keyboard, or a mouse.

[0044] The communication unit 55 communicates with the video streaming site server device 11 and the management server device 21 .

[0045] The Bluetooth (registered trademark) communication unit 57 performs Bluetooth (registered trademark) communication with an external device.

[0046] The memory 59 stores a program PRG1 executed by the processing unit 61 .

[0047] Processing unit 61 executes program PRG1 stored in memory 59 to perform the processing of communication terminal device 11 defined in the present embodiment.

[0048] Figure 2 2 is a functional block diagram of the server device 21 according to the embodiment of the present invention.

[0049] like Figure 2 As shown, the server device 21 includes, for example, a communication unit 75 , a memory 79 , and a processing unit 81 .

[0050] The memory 79 stores a program PRG2 executed by the processing unit 81 .

[0051] 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.

[0052] The following describes the flow of the indoor layout support method according to this embodiment.

[0053] <Overall Action>

[0054] Figure 3This is a flowchart for explaining the overall flow of the indoor layout support method according to the first embodiment of the present invention.

[0055] Each step will be explained.

[0056] The processing unit 81 of the server device 21 may execute part or all of the processing executed by the processing unit 61 of the communication terminal device 11 described in this specification.

[0057] Step ST11:

[0058] The communication terminal device 11, under the control of the processing unit 61, scans the room by irradiating the laser from the laser 50 into the room, and generates 3D image data of the room based on the scanning result and the shooting result of the camera 51. Then, the processing unit 61 converts the 3D image data into 3D CAD data.

[0059] The processing unit 61 displays a 3D CAD image of the interior of the room corresponding to the 3D CAD data on the display 52. ​​For example, Figure 3 CAD image shown.

[0060] Here, 3D image data is obtained by synthesizing mesh data and camera shooting results. CAD data is CAD format data that is completely converted into computer graphics and does not include mesh or photo data.

[0061] In addition, the communication terminal device 11 may acquire the CAD data from the outside instead of generating the CAD data.

[0062] At this time, the processing unit 61 determines the area where the point set has been acquired and the area where the point set has not been acquired in the room based on the indoor position of the communication terminal device 11 and the captured image of the camera 51. Then, the processing unit 61 displays the area where the point set has been acquired and the area where the point set has not been acquired in different colors on the screen of the display 52. ​​In this way, the point sets of all necessary areas in the room can be obtained without omission.

[0063] Step ST12:

[0064] The user operates the operation unit 53 to designate and select a category (type) of merchandise such as furniture and household appliances to be placed in the room.

[0065] For example, the processing unit 61 may display a commodity category selection bar on the display 52, from which the user may select, or the user may directly input a commodity category, such as a refrigerator, a shelf, a bookcase, a sofa, or the like.

[0066] Step ST13:

[0067] Based on the CAD data generated or acquired in step ST11 and the type and budget of the commodity specified and selected in step ST12, the processing unit 61 determines a plurality of candidate configurations showing different combinations of indoor spaces where the commodity can be placed and the commodity placed in the space. In each candidate configuration, the commodity and the location where the commodity is placed are different from each other. In this case, even if the commodity type is the same, the candidate items for placing different commodities may be different candidate configurations.

[0068] Based on the CAD data in step ST11, the processing unit 61 determines the size of the space in the room where the goods can be placed. Specifically, the width, depth, height, etc. are determined.

[0069] In addition, the processing unit 61 obtains information from an external server as to whether there are any products that can be placed in the indoor space based on the budget and the above-determined size, and if there are any, obtains data on the products.

[0070] For example, the processing section 61 accesses an external server to search for candidate commodities within the budget according to the commodity category specified in step ST12 , and obtains the sizes of the candidate commodities.

[0071] Next, the processing unit 61 determines a space in the indoor space where the candidate commodity of the acquired size can be placed. If there are a plurality of such determined spaces, the processing unit 61 determines a candidate arrangement for each space.

[0072] If multiple product categories are specified, a space that can be placed is determined for each product. At this time, candidate configurations are determined so that products of different categories are not placed in the same space.

[0073] Step ST14:

[0074] The processing unit 61 executes processing for displaying a 3D CAD image of a commodity placed in a space where the commodity can be placed, for each of the plurality of candidate arrangements determined in step ST13 .

[0075] The user studies which pattern (combination of commodities and their placement) is good and performs determination operation while observing CAD images of a plurality of candidate configurations.

[0076] Furthermore, when the user operates the operation unit 53 to designate a commodity on the CAD image, the processing unit 61 displays a selection screen for selecting other commodities in the same category as the displayed commodity on the display 52 .

[0077] When another product is selected on the selection screen, the processing unit 61 generates CAD data of the other product after placement, and displays a CAD image based on the CAD data.

[0078] Step ST15:

[0079] When the user operates the operation section 53 to request purchase of the commodity selected on the CAD image displayed in step ST14 , the processing section 61 proceeds to step ST16 .

[0080] Step ST16:

[0081] When a purchase request is input in step ST15 , the processing unit 61 executes a purchase process for the commodity selected by the user.

[0082] The communication terminal device 11 accesses the server device 21 or other commodity sales sites via the communication unit 55 to execute purchase processing.

[0083] <Purchasing actions for flooring and other products>

[0084] Figure 4 This is a flowchart for explaining the flow of a purchase operation of a commodity such as a floor covering in the interior layout support method according to the first embodiment of the present invention.

[0085] Each step will be explained.

[0086] Step ST21:

[0087] According to Figure 3 The user operates the operation unit 53 to designate a product such as a wall or floor in the room on the CAD image of the room displayed on the display 52 based on the CAD data generated in step ST11 shown in the figure.

[0088] Step ST22:

[0089] The processing unit 61 specifies the category (type) of the merchandise specified in step ST21 , and acquires supplier information indicating suppliers that provide merchandise associated in advance with the merchandise category.

[0090] Then, the processing unit 61 provides the CAD data corresponding to the CAD image for the designated operation to the communication device of the supplier based on the acquired supplier information.

[0091] The CAD data may be provided by sending the CAD data to an email address of the supplier, or by uploading it to a system accessible to the supplier.

[0092] Step ST23:

[0093] The supplier transmits product data of a plurality of candidate products (candidate product data) to the communication terminal device 11 based on the CAD data acquired in step ST22 .

[0094] The processing unit 61 stores the plurality of candidate product data received from the supplier in the memory 59 .

[0095] Furthermore, the communication terminal device 11 can acquire product data, the price of the product, and estimated information of the construction cost from the supplier, and display the information on the display 52 .

[0096] Step ST24:

[0097] The processing unit 61 generates new CAD data by placing an image corresponding to the candidate product data at the product position based on the indoor CAD data and the candidate product data stored in the memory 59 in step ST23, and generates a CAD image corresponding to the CAD data.

[0098] The generation of the CAD image is executed for each of a plurality of candidate product data.

[0099] Step ST25:

[0100] When the user operates the operation section 53 to request purchase of the commodity selected on the CAD image displayed in step ST23 , the processing section 61 proceeds to step ST25 .

[0101] Step ST26:

[0102] When a purchase request is input in step ST24, the processing unit 61 executes a purchase process for the commodity selected by the user.

[0103] <First Modification of First Embodiment>

[0104] Through the processing of the first embodiment, the processing unit 61 associates the indoor CAD data of the user who has purchased goods in the past, the candidate configuration selected by the user, and the goods purchased by the user, and stores them in the memory 59 as history data.

[0105] When Figure 3 In step ST14 shown, when multiple candidate CAD images are displayed on the display 52, the processing unit 61 determines, based on the historical data stored in the memory 59, candidate configurations and products that have been selected by other users and have a high degree of similarity with the combination of indoor CAD data generated or acquired in step ST11 and the product category and its budget specified in step ST12, as candidate configurations and products with high priority.

[0106] A high similarity means, for example, that the more common points there are in the indoor area and shape according to the CAD data, the more common points there are in the designated product category, or the closer the budgets are, the higher the similarity.

[0107] Then, in step ST13 , the candidate arrangement and the product having a high similarity are displayed on the display 52 as CAD images with higher priority.

[0108] The high priority display means that the priority may be indicated by numbers or the like, or the items displayed on the screen in descending order of priority may be displayed at the top.

[0109] This allows the user to preferentially display candidate configurations and products that are more likely to be selected by the user, allowing the user to make decisions efficiently.

[0110] <Second Modification of First Embodiment>

[0111] exist Figure 3 In step ST13 shown, the processing unit 61 may determine candidate configurations and candidate purchase products using the learned model.

[0112] Figure 5 It is a diagram for explaining a second variation example of determining candidate configurations and candidate purchase products using a learned model.

[0113] That is, the processing unit 61 uses the learned model to execute Figure 3 The process of step ST13 is shown.

[0114] The learned model is obtained by performing machine learning using a combination of indoor CAD data of a user who has purchased goods in the past, candidate configurations selected by the user, goods purchased by the user, and attributes of the user as training data.

[0115] The processing unit 61 may create a learned model, or relearn the model based on newly acquired information. Alternatively, the learned model may be generated by other systems.

[0116] The processing unit 61 inputs the CAD data and the attributes of the user into the learning model, and outputs information on candidate configurations and candidate purchase products. The output may be only the information with the highest priority, or multiple information may be output in a priority order.

[0117] In step ST14 , the processing unit 61 generates a candidate CAD image based on the output of the learning model and the CAD data.

[0118] In this way, by using the learned model, as the amount of training data increases, candidates suitable for the user's attributes and the like can be determined.

[0119] The second variation can also be combined with the functions of the first variation described above.

[0120] By adopting a learning model in this way, as actual results increase, more suitable candidate configurations and purchase candidates can be displayed to the user.

[0121] <Third Modification of First Embodiment>

[0122] In the third variation, after the processing of the first embodiment, the processing unit 61 associates the indoor CAD data of the user who has purchased goods in the past, the goods purchased by the user, and the prices of the purchased goods, and stores them in the memory 59 as history data.

[0123] In addition, Figure 3 In step ST13 shown, the processing unit 61 performs machine learning using the combination of the CAD data of the indoor environment of a user who has purchased products in the past and the products purchased by the user as training data to determine candidate configurations and products.

[0124] By adopting the learning model in this way, as the actual results increase, more suitable candidate configurations and purchase candidates can be displayed to the user.

[0125] Figure 6 This is a diagram for explaining the learning model used in the third variation.

[0126] like Figure 6 As shown, in this learning model, the CAD data of the user's room and the commodity data designated by the user are input, and placement candidates and purchase candidates of the commodity are output.

[0127] Next, the processing unit 61 performs the following processing: Figure 3 The historical data stored in the memory 59 in the step ST16 shown will determine, among the above-mentioned candidate configurations and products that have been determined, the candidate configurations and products that have indoor CAD data, a combination with a high degree of similarity to the category specified by the user and its budget, and have been selected by other users as high-priority candidate configurations and products for display.

[0128] A high similarity means, for example, that the more common points there are in the indoor area and shape according to the CAD data, the more common points there are in the designated product category and budget (purchase price), or the closer the budgets are, the higher the similarity.

[0129] The processing unit 61 gives higher priority to displaying the candidate arrangement and the product having a high similarity as a CAD image on the display 52 .

[0130] The high priority display means that the priority may be expressed by numbers or the like, or that the items displayed on the screen in descending order of priority may be displayed at the top.

[0131] This allows the user to preferentially display candidate configurations and products that are more likely to be selected by the user, allowing the user to make decisions efficiently.

[0132] Note that the third variation example is the same as the contents described in the first embodiment and the like except for the contents described as this third variation example.

[0133] According to the third variation, in addition to the structure of using the learning model to determine the candidate configurations and products that are more suitable for the user's situation, the candidate configurations and products selected by the user that have the CAD data of the room and a high degree of similarity with the combination of the specified category and its budget are also determined as "high priority" candidate configurations and products. That is, the determination of the candidates and the determination of the high-priority candidates are divided into two stages. As a result, users can more easily select candidate configurations and products that are suitable for themselves.

[0134] Below Figure 3 A method of generating indoor 3D CAD data using the communication terminal device 11 in step ST11 shown in FIG. 1 will be described.

[0135] Figure 7 It is used to illustrate Figure 3 A flowchart showing details of a method for generating a 3D indoor image is shown.

[0136] The following will explain Figure 4 Each step in .

[0137] Step ST41:

[0138] The processing unit 61 of the communication terminal device 11 recognizes (estimates) the indoor position of the communication terminal device 11. For estimating the own position, for example, the own position estimation technology based on LiDAR is used.

[0139] Step ST42:

[0140] The user moves the communication terminal device 11, irradiates the entire area or a part of the room with laser light from the laser 50, and detects the reflection. Then, the processing unit 61 generates point set data of the room and the objects in the room based on the detection results.

[0141] At the same time, the camera 51 captures an image of the laser irradiation position irradiated by the laser 50 .

[0142] Step ST43:

[0143] The processing unit 61 converts the point set data generated in step ST42 from the coordinate system of the position of the communication terminal device 11 determined in step ST41 into the indoor coordinate system.

[0144] Step ST44:

[0145] The processing unit 61 extracts valid data (deletes unnecessary data) from the point set data converted in step ST43. This extraction process is performed by deleting point sets having the same or similar attributes as the point sets determined in advance to be unnecessary.

[0146] Step ST45:

[0147] The processing unit 61 converts the point set data subjected to the extraction process in step ST44 into mesh (polygon) data.

[0148] Step ST46:

[0149] The processing unit 61 converts the mesh data generated in step ST45 into surface (plane) data. This conversion uses a predetermined plane detection technique.

[0150] Step ST47:

[0151] The processing unit 61 generates 3D image data (3D image) by pasting the captured image of the camera 51 acquired in step ST42 onto the mesh data based on the surface data generated in step ST46 .

[0152] Step ST48:

[0153] The processing unit 61 converts the 3D image data generated in step ST47 into 3D CAD data and displays it on the display 52 .

[0154] The processing unit 61 determines (measures) the dimensions (height, length and width, layout) in the room based on the 3D CAD data, and displays the dimensions on the 3D CAD image.

[0155] Furthermore, through the above-described processing, CAD data can be generated as long as the wall is recognized, so a 3D image of a layout without indoor furniture or the like can be generated.

[0156] It should be noted that a learning model may be used in some steps of the above processing.

[0157] According to the indoor layout support method of this embodiment, 3D conversion can be performed instantly, easily and with high accuracy anywhere by using a communication terminal device 11 such as a smartphone, thus contributing to the application or widespread popularization of 3D technology in all fields.

[0158] For example, it can be used in businesses in the real estate industry such as the renovation industry, construction companies, and the moving industry.

[0159] In addition, the indoor layout support method according to the present embodiment can improve the labor shortage in the construction industry and the experience during decoration.

[0160] That is, if 3D scanners can replace the measurement and drawing that were previously done manually, human resources can be allocated to other more advanced businesses, thereby providing higher value-added goods, which will also achieve cost reductions.

[0161] In addition, for the staff themselves, an environment has been created where they can focus on the work they are responsible for, and the work of craftsmen, which previously had to be learned through experience, has become easier and clearer to a certain extent. It can also contribute to the recruitment and training of young talents who will take on responsibilities in the future.

[0162] By using 3D scanning technology, measurements can be completed very easily and not time-consuming.

[0163] Furthermore, by adding color to stereo images, inserting photos, and viewing 360 degrees from the front, back, left, and right, everyone involved can conduct business with the same generated image, rather than an image that exists only in everyone's mind.

[0164] It also reduces paper waste by being able to manage it on-device. These efficiency gains will ultimately benefit consumers, allowing them to furnish their buildings more affordably than before.

[0165] Furthermore, according to the present 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 perform 3D scanning easily and inexpensively.

[0166] According to the present embodiment, by simply shooting a video of the room using the communication terminal device 11, it is possible to acquire and store measurable 3D models and photo data.

[0167] Furthermore, according to the present embodiment, it is possible to output to a personal computer and share it in various formats such as a point set or a grid.

[0168] Furthermore, according to the communication terminal device 11, by Figure 4 It can process data with higher accuracy than a tape measure.

[0169] Furthermore, according to the present embodiment, multiple different modes of candidate configurations can be provided to the user according to the type of merchandise the user wishes to purchase and the budget, and candidate configurations that the user would not have thought of can be added to the research objects.

[0170] Since multiple candidate configurations and candidate products can be determined at the same time, and a CAD image with an image of the product placed in each candidate configuration is provided to the user, the user can have a more concrete impression of placing the product in each candidate configuration based on the CAD image, avoiding a gap between the impression and reality.

[0171] Furthermore, according to the present embodiment, the user only needs to perform an operation of designating a product such as a floor on the CAD image, and based on the product data from the product supplier, the user can easily grasp the impression of the room when the product is used from the CAD image.

[0172] <Second Embodiment>

[0173] Figure 8 This is a flowchart for explaining the overall flow of the indoor layout support method according to the second embodiment of the present invention.

[0174] Each step will be explained.

[0175] The processing unit 81 of the server device 21 may execute part or all of the processing executed by the processing unit 61 of the communication terminal device 11 described in this specification.

[0176] Step ST51:

[0177] Communication terminal device 11, under control of processing unit 61, irradiates laser light from laser 50 into the room to scan the room, and generates 3D image data of the room based on the scanning result and the image capturing result of camera 51. Processing unit 61 then converts the 3D image data into 3D CAD data.

[0178] The processing unit 61 displays a 3D CAD image of the room corresponding to the 3D CAD data on the display 52. ​​For example, Figure 3 CAD image shown.

[0179] Here, 3D image data is obtained by synthesizing mesh data and camera shooting results. CAD data is CAD format data that has been completely converted into computer graphics, excluding mesh and photo data.

[0180] In addition, the communication terminal device 11 may acquire the CAD data from the outside instead of generating the CAD data.

[0181] At this time, the processing unit 61 determines the area where the point set has been acquired and the area where the point set has not been acquired in the room based on the indoor position of the communication terminal device 11 and the captured image of the camera 51. Then, the processing unit 61 displays the area where the point set has been acquired and the area where the point set has not been acquired in different colors on the screen of the display 52. ​​In this way, the point sets of all necessary areas in the room can be obtained.

[0182] Step ST52:

[0183] On the CAD image displayed in step ST51, the user operates the operation unit 53 to designate a space in the room where the product is to be placed. The space is designated by moving a cursor so as to surround the area.

[0184] Step ST53:

[0185] The user operates the operation unit 53 to input the commodity category of the commodity to be placed in the space specified in step ST52. This input can be performed by displaying a commodity category selection bar on the display 52 and allowing the user to select from there, or the user can directly input the commodity category. The commodity category is a commodity category such as a refrigerator, a shelf, a bookcase, a sofa, etc.

[0186] Step ST54:

[0187] Based on the CAD data in step ST51, processing unit 61 of communication terminal device 11 determines the size of the space designated in step ST52. Specifically, the width, depth, height, etc. are determined.

[0188] Step ST55:

[0189] The processing unit 61 searches for products that can be placed in the space of the size specified in step ST54 among the products belonging to the product category input in step ST53. In this search, the user can specify at least one of a budget, a product brand, and a function, and can search for products that match the specified content.

[0190] The processing unit 61 displays a product selection screen showing the search results on the display 52 .

[0191] The user selects a product using the product selection screen.

[0192] The communication terminal device 11 accesses the server device 21 or other product sales websites and displays the search results.

[0193] Step ST56:

[0194] The processing unit 61 displays on the display 52 a 3D CAD image in which the product selected in step ST55 is placed in the space designated in step ST52 .

[0195] Step ST57:

[0196] When the user operates the operation unit 53 to request purchase of the commodity selected in step ST55 , the processing section 61 proceeds to step ST58 .

[0197] When a purchase request is input in step ST57, the processing unit 61 executes a purchase process for the commodity selected by the user.

[0198] The communication terminal device 11 accesses the server device 21 or other commodity sales sites via the communication unit 55 to execute purchase processing.

[0199] <Third Embodiment>

[0200] In the above embodiment, the case of purchasing goods to be placed in a room after scanning is exemplified. In the present embodiment, a case of updating goods (materials) such as walls and floors in a laboratory will be described.

[0201] The following describes the flow of the indoor layout support method according to this embodiment.

[0202] Fig. 9 This is a flowchart for explaining the overall flow of the indoor layout support method according to the third embodiment of the present invention.

[0203] Each step will be explained.

[0204] Step ST61:

[0205] Communication terminal device 11, under control of processing unit 61, irradiates laser light from laser 50 into the room to scan the room, and generates 3D image data of the room based on the scanning result and the image capturing result of camera 51. Processing unit 61 then converts the 3D image data into 3D CAD data.

[0206] This step ST61 is the same as the processing of step ST11 described in the first embodiment.

[0207] Step ST62:

[0208] The user operates the operation unit 53 to designate a commodity (building material) such as an indoor floor or wall on the CAD image displayed in step ST61. The designation is performed by clicking the image of the designated commodity with a cursor or by moving the cursor to surround the area.

[0209] Step ST63:

[0210] The user operates the operation unit 53 to input the commodity category of the commodity specified in step ST62. This input can be performed by displaying a commodity category selection bar on the display 52 and allowing the user to select from there, or the user can directly input the commodity category. The commodity category is, for example, a commodity category such as wall or floor.

[0211] Step ST64:

[0212] The processing unit 61 of the communication terminal device 11 sends (shares) the product specified by the user in step ST61 together with the 3D image to the supplier (design, decoration, construction, etc. supplier) that has been previously associated with the product category specified by the user in step ST63. At this time, the communication terminal device 11 sends information on the size of the product (wall size, floor size, etc.) to the supplier's terminal device. In addition, 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.

[0213] Step ST65:

[0214] The supplier recommends products suitable for the user's interior based on the products, user's requirements and CAD images shared in step ST64, 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 a CAD image using the product image. In addition, the recommendation information about the product can be sent from the supplier's terminal device to the communication terminal device 11.

[0215] As described above, according to the interior layout support method of the second embodiment, even when it comes to commodities such as interior building materials, the user can request a supplier to make a proposal and offer by simply specifying the commodity from a CAD image of the interior using the communication terminal device 11.

[0216] In addition, since suppliers can obtain 3D images of the user's interior, they can make more reasonable suggestions and quotations, including renovations.

[0217] Furthermore, since suppliers such as design firms can use the functions of the communication terminal device 11, they can make more appropriate proposals.

[0218] The present invention is not limited to the above-described embodiments.

[0219] That is, those skilled in the art can make various changes, combinations, sub-combinations and substitutions to the constituent elements of the above-described embodiments within the technical scope of the present invention or its equivalent scope.

[0220] Industrial Applicability

[0221] The present invention can be applied to a system for selecting commodities to be placed indoors.

[0222] (Explanation of Reference Numerals)

[0223] 11...Communication terminal device

[0224] 21...Server device

[0225] 50...Laser

[0226] 51...Camera

[0227] 52...Display

[0228] 53...Operation Department

[0229] 55...Ministry of Communications

[0230] 57...Bluetooth (registered trademark) communication unit

[0231] 61...Processing Department

[0232] 75...Ministry of Communications

[0233] 79...Memory

Claims

1. An indoor layout support method, comprising: executing the following steps by a computer: The first step is to obtain or generate indoor 3D CAD data; The second step is to receive, from the user, a designation of a category and a budget of at least one commodity of furniture and household appliances to be placed in the room; A third step is to determine, based on the CAD data acquired or generated in the first step and the category and budget of the commodity specified in the second step, a plurality of candidate configurations showing spaces in the room where the commodity can be placed and commodities to be placed in the spaces in different combinations; A fourth step is to display, for each of the plurality of candidate configurations determined in the third step, 3D CAD images of the products placed in the spaces that can be placed; A fifth step, when the user specifies a product on the CAD image displayed in the fourth step, a selection screen for selecting other products of the same category as the displayed product is displayed, and when other products are selected on the selection screen, the CAD data in which the other products are placed is generated; The sixth step is to associate the CAD data of the room of the user who has purchased goods in the past, the goods purchased by the user and the price of the purchased goods, and store them in the memory as historical data. Wherein, in the third step, The candidate configuration and the product are determined using a learning model obtained by machine learning using a combination of the CAD data of the indoor environment of a user who has purchased products in the past and the products purchased by the user as training data, 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, the candidate configurations and products selected by other users that have a high similarity in combination with the indoor CAD data, the specified category and its budget are displayed as high-priority candidate configurations and products.

2. The indoor layout support method according to claim 1, wherein: Further, the computer performs the following steps: In a seventh step, based on the CAD data, receiving a designation of at least one commodity among the floor and the wall in the room from a user; Step 8: transmitting the CAD data to a supplier that is pre-associated with the commodity specified in step 7; In a ninth step, receiving product data on the candidate products specified in the seventh step from the supplier, Wherein, in the fourth step, the CAD image is displayed according to the commodity data received in the ninth step.

3. The indoor layout support method according to claim 1, wherein: In the second step, a designation of a family structure of the user is received from the user, In the third step, a plurality of candidate configurations are determined according to 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 the step of causing the user to purchase the purchase candidate according to an instruction from the user.

5. The indoor layout support method according to claim 1, wherein: In the first step, the computer performs the following steps: The eleventh step is to determine the indoor location of the main device for laser irradiation; The twelfth step is to generate a point set of the room and the indoor objects by the laser irradiation; The thirteenth step is to convert the generated point set from the determined coordinate system of the main device into an indoor coordinate system; The fourteenth step is to extract valid data from the point set; 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; Step 17, according to the surface data, pasting the captured image onto the grid data to generate 3D image data; In the eighteenth step, the 3D image data generated in the seventeenth step is converted into the 3D CAD data.

6. The indoor layout support method according to claim 5, wherein: The shape and size of the room are determined based on the surface data, and the size is displayed in the display image.

7. The indoor layout support method according to claim 5, wherein: Based on the position of the main device and the captured image, the area in the room where the point set has been acquired and the area where the point set has not been acquired are determined, and the area where the point set has been acquired and the area where the point set has not been acquired are displayed on the screen in different colors.

8. An indoor layout support method, comprising: executing the following steps by a computer: The first step is to obtain or generate indoor 3D CAD data; The second step is to receive, from the user, a designation of a category and a budget of at least one commodity of furniture and household appliances to be placed in the room; A third step is to determine, based on the CAD data acquired or generated in the first step and the category and budget of the commodity specified in the second step, a plurality of candidate configurations showing spaces in the room where the commodity can be placed and commodities to be placed in the spaces in different combinations; A fourth step is to display, for each of the plurality of candidate configurations determined in the third step, 3D CAD images of the products placed in the spaces that can be placed; A fifth step, when the user specifies a product on the CAD image displayed in the fourth step, a selection screen for selecting other products of the same category as the displayed product is displayed, and when other products are selected on the selection screen, the CAD data in which the other products are placed is generated; The sixth step is to associate the CAD data of the interior of the user who has purchased the product in the past, the candidate configuration selected by the user, and the product purchased by the user, and store them in the memory as historical data. Wherein, in the third step, based on the historical data stored in the memory in the sixth step, the candidate configurations and products having the indoor CAD data, the combination with a high similarity to the combination of the specified category and its budget, and the candidate configurations and products selected by other users are determined as the candidate configurations and products with high priority, In the third step, the candidate configurations and products are determined using a learning model obtained by machine learning using a combination of the CAD data of the interior of a user who has purchased products in the past, the candidate configurations selected by the user, the products purchased by the user, and the user's attributes as training data.

9. A program causing a computer to execute the following steps: The first step is to obtain or generate indoor 3D CAD data; The second step is to receive, from the user, a designation of a category and a budget of at least one commodity of furniture and household appliances to be placed in the room; A third step is to determine, based on the CAD data acquired or generated in the first step and the category and budget of the commodity specified in the second step, a plurality of candidate configurations showing spaces in the room where the commodity can be placed and commodities to be placed in the spaces in different combinations; A fourth step is to display, for each of the plurality of candidate configurations determined in the third step, 3D CAD images of the products placed in the spaces that can be placed; A fifth step, when the user specifies a product on the CAD image displayed in the fourth step, a selection screen for selecting other products of the same category as the displayed product is displayed, and when other products are selected on the selection screen, the CAD data in which the other products are placed is generated; The sixth step is to associate the CAD data of the room of the user who has purchased goods in the past, the goods purchased by the user and the price of the purchased goods, and store them in the memory as historical data. Wherein, in the third step, The candidate configuration and the product are determined using a learning model obtained by machine learning using a combination of the CAD data of the indoor environment of a user who has purchased products in the past and the products purchased by the user as training data, 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, the candidate configurations and products that have the CAD data of the room, a combination with a high degree of similarity to the specified category and its budget, and have been selected by other users are displayed as high-priority candidate configurations and products.

10. A program causing a computer to execute the following steps: The first step is to obtain or generate indoor 3D CAD data; The second step is to receive, from the user, a designation of a category and a budget of at least one commodity of furniture and household appliances to be placed in the room; A third step is to determine, based on the CAD data acquired or generated in the first step and the category and budget of the commodity specified in the second step, a plurality of candidate configurations showing spaces in the room where the commodity can be placed and commodities to be placed in the spaces in different combinations; A fourth step is to display, for each of the plurality of candidate configurations determined in the third step, 3D CAD images of the products placed in the spaces that can be placed; A fifth step, when the user specifies a product on the CAD image displayed in the fourth step, a selection screen for selecting other products of the same category as the displayed product is displayed, and when other products are selected on the selection screen, the CAD data in which the other products are placed is generated; The sixth step is to associate the CAD data of the interior of the user who has purchased the product in the past, the candidate configuration selected by the user, and the product purchased by the user, and store them in the memory as historical data. Wherein, in the third step, based on the historical data stored in the memory in the sixth step, the candidate configurations and commodities selected by other users, which have the CAD data of the room and a high degree of similarity to the combination of the specified category and its budget, are determined as the candidate configurations and commodities with high priority, In the third step, the candidate configurations and products are determined using a learning model obtained by machine learning using a combination of the CAD data of the interior of a user who has purchased products in the past, the candidate configurations selected by the user, the products purchased by the user, and the user's attributes as training data.

11. An indoor layout support system, comprising: A first unit, which acquires or generates 3D CAD data of the interior; a second unit that receives, from a user, a designation of a category and a budget of at least one of furniture and home appliances to be placed in the room; a third unit that determines, based on the CAD data acquired or generated in the first unit and the category and budget of the commodity specified in the second unit, a plurality of candidate configurations showing spaces in the room where the commodity can be placed and commodities to be placed in the spaces in different combinations; a fourth unit for displaying, for each of the plurality of candidate configurations determined in the third unit, 3D CAD images of the product placed in the space where the product can be placed; a fifth unit that, when a user designates a product on the CAD image displayed by the fourth unit, displays a selection screen for selecting other products of the same category as the displayed product, and, when other products are selected on the selection screen, generates the CAD data in which the other products are placed; A sixth unit associates the CAD data of the room of a user who has purchased goods in the past, the goods purchased by the user, and the price of the purchased goods, and stores them in a memory as historical data, Wherein, in the third unit, The candidate configuration and the product are determined using a learning model obtained by machine learning using a combination of the CAD data of the indoor environment of a user who has purchased products in the past and the products purchased by the user as training data, 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, the candidate configurations and products that have the CAD data of the room, the combination with a high similarity to the specified category and its budget, and have been selected by other users are displayed as the candidate configurations and products with high priority.

12. An indoor layout support system, comprising: A first unit, which acquires or generates 3D CAD data of the interior; a second unit that receives, from a user, a designation of a category and a budget of at least one of furniture and home appliances to be placed in the room; a third unit that determines, based on the CAD data acquired or generated in the first unit and the category and budget of the commodity specified in the second unit, a plurality of candidate configurations showing spaces in the room where the commodity can be placed and commodities to be placed in the spaces in different combinations; a fourth unit for displaying, for each of the plurality of candidate configurations determined in the third unit, 3D CAD images of the product placed in the space where the product can be placed; a fifth unit that, when a user designates a product on the CAD image displayed by the fourth unit, displays a selection screen for selecting other products of the same category as the displayed product, and, when other products are selected on the selection screen, generates the CAD data in which the other products are placed; The sixth unit associates the CAD data of the interior of a user who has purchased goods in the past, the candidate configuration selected by the user, and the goods purchased by the user, and stores them in a memory as historical data. The third unit determines 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 as the candidate configurations and products with high priority based on the historical data stored in the memory in the sixth unit, The third unit determines the candidate configurations and products using a learning model obtained by machine learning using a combination of the CAD data of the interior of a user who has purchased products in the past, the candidate configurations selected by the user, the products purchased by the user, and the attributes of the user as training data.

Images