Wall material identification method and device based on laser point cloud scanning, and electronic device

Abstract

Embodiments of the present disclosure disclose a wall material identification method and device based on laser point cloud scanning, and an electronic device. A specific embodiment of the method includes: controlling a shooting device to perform image shooting on original house walls to generate a set of original house wall images; performing wall damage identification on each original house wall image in the set of original house wall images to generate a set of wall damage image features; controlling a three-dimensional laser scanning device to perform laser scanning on the house walls after the demolition of the decoration layer to generate house wall point cloud data; performing point cloud file analysis on the house wall point cloud data to generate a set of house wall point cloud data; performing wall material identification on each wall surface of the original house after the demolition of the decoration layer according to the set of wall damage image features and the set of house wall point cloud data to generate a set of wall material identification information; and storing the set of wall material identification information. The embodiment can reduce the occupation of computing resources.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the technical field of wall material identification, and particularly relate to a wall material identification method and device based on laser point cloud scanning and an electronic device. BACKGROUND

[0002] House wall material identification is a technology for identifying wall material information. The traditional interior decoration design workflow is as follows: a designer goes to a proposed signing project to conduct on-site house measurement, briefly records the positional relationship and spatial size information of each room, i.e., preliminary house measurement. After the original decoration is removed to a bare state, a construction personnel goes to the site to conduct accurate measurement, assesses the present wall material information, and then proposes a corresponding wall construction method scheme.

[0003] However, in practice, it is found that when the above method is used to identify the wall material of a house, the following technical problems often exist:

[0004] The artificial detection method is greatly affected by human factors, has low accuracy and low efficiency. Although the traditional detection instrument method has improved accuracy, it still needs manual operation and is difficult to comprehensively and deeply obtain the three-dimensional information and internal conditions of the wall. Therefore, multiple measurements are required to ensure accuracy. As a result, more storage resources are occupied.

[0005] The above information disclosed in the background section is only used to enhance the understanding of the background of the present inventive concept, and therefore, it can contain information that does not form the prior art known to those of ordinary skill in the art in the country. SUMMARY

[0006] The summary section is provided to introduce the concepts briefly in a simplified form, which will be described in detail in the specific embodiments section later. The summary section is not intended to identify key or essential features of the claimed technology nor is it intended to be used to limit the scope of the claimed technology.

[0007] Some embodiments of the present disclosure propose a wall material identification method and device based on laser point cloud scanning and an electronic device to solve one or more of the technical problems mentioned in the background section.

[0008] In a first aspect, some embodiments of the present disclosure provide a wall material identification method based on laser point cloud scanning, which comprises: controlling a photographing device to perform image photographing on original house walls to generate a set of original house wall images; identifying wall damage of each original house wall image in the set of original house wall images to generate a set of wall damage image features, wherein each wall damage image feature corresponds to a wall of the original house; controlling a three-dimensional laser scanning device to perform laser scanning on the walls of the house after the demolition of the decoration layer to generate house wall point cloud data; performing point cloud file analysis on the house wall point cloud data to generate a set of house wall point cloud data, wherein each house wall point cloud data corresponds to a wall of the original house after the demolition of the decoration layer; identifying the material of each wall of the original house after the demolition of the decoration layer according to the set of wall damage image features and the set of house wall point cloud data to generate a set of wall material identification information, and storing the set of wall material identification information, wherein each wall material information in the set of wall material information comprises at least one of the following: wall material data, wall age data, and wall integrity data.

[0009] In a second aspect, some embodiments of the present disclosure provide a wall material identification device based on laser point cloud scanning, which comprises: an image photographing unit configured to control a photographing device to perform image photographing on original house walls to generate a set of original house wall images; a wall damage identification unit configured to identify wall damage of each original house wall image in the set of original house wall images to generate a set of wall damage image features, wherein each wall damage image feature corresponds to a wall of the original house; a laser scanning unit configured to control a three-dimensional laser scanning device to perform laser scanning on the walls of the house after the demolition of the decoration layer to generate house wall point cloud data; a file analysis unit configured to perform point cloud file analysis on the house wall point cloud data to generate a set of house wall point cloud data, wherein each house wall point cloud feature corresponds to a wall of the original house after the demolition of the decoration layer; a wall material identification unit configured to identify the material of each wall of the original house after the demolition of the decoration layer according to the set of wall damage image features and the set of house wall point cloud data to generate a set of wall material identification information, and store the set of wall material identification information, wherein each wall material information in the set of wall material information comprises at least one of the following: wall material data, wall age data, and wall integrity data.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the first aspect.

[0011] In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any implementation manner of the first aspect.

[0012] The above various embodiments of the present disclosure have the following beneficial effects: the wall material identification method based on laser point cloud scanning of some embodiments of the present disclosure can reduce the storage resource occupation of the generated wall material identification information. Specifically, the reason for occupying more storage resources is that the artificial detection method is greatly affected by human factors, has low accuracy and low efficiency. And although the traditional detection instrument method has improved accuracy, it still needs manual operation, and it is difficult to comprehensively and deeply obtain the three-dimensional information and internal condition of the wall. Therefore, multiple measurements are needed to ensure accuracy. Based on this, the wall material identification method based on laser point cloud scanning of some embodiments of the present disclosure, first, considering that the original house wall is prone to abnormal conditions. Therefore, by controlling the shooting device, the original house wall is imaged to generate an original house wall image set. Thus, the wall damage identification can be performed from the appearance of the original wall without removing the decoration layer. Thus, a wall damage image feature set can be generated. Then, considering the house wall after removing the decoration layer, even if the surface image can be shot, the internal condition is difficult to detect. Therefore, by controlling the three-dimensional laser scanning device, the house wall after removing the decoration layer is laser scanned to generate house wall point cloud data. Then, the house wall point cloud data can be analyzed to generate a house wall point cloud data set. Thus, the wall damage image features and the house wall point cloud data corresponding to each wall can be determined. Thus, the wall material identification can be used to comprehensively judge the material information of the wall, and improve the accuracy of the generated material information. Finally, the wall material identification information set can be stored. Here, each wall material information can include wall material data, wall age data, and wall integrity data. Further, not only the accuracy of the identification can be determined, but also the diversity of the wall material identification information can be improved. Thus, multiple measurements and storage of the results of multiple measurements can not be needed. Further, the occupation of computing resources can be reduced. BRIEF DESCRIPTION OF DRAWINGS

[0013] The above and other features, aspects and advantages of the present disclosure will become more apparent after a reading of the following detailed description together with the accompanying drawings. Throughout the drawings, similar or same reference numerals are used to denote similar or same elements. It should be understood that the drawings are schematic and elements and features do not necessarily appear to scale.

[0014] Figure 1 is a flowchart of some embodiments of a wall material recognition method based on laser point cloud scanning according to the present disclosure;

[0015] Figure 2 is a rendered three-dimensional structure diagram of a house according to some embodiments of a wall material recognition method based on laser point cloud scanning according to the present disclosure;

[0016] Figure 3 is a structural diagram of some embodiments of a wall material recognition device according to the present disclosure;

[0017] Figure 4 is a structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0018] Embodiments of the present disclosure will be described in more detail with reference to the drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly and completely understood. It should be understood that the drawings and embodiments of the present disclosure are only for illustrative purposes and are not intended to limit the scope of protection of the present disclosure.

[0019] It should also be noted that, for the sake of brevity, only the parts of the drawings that are relevant to the present disclosure are shown. The embodiments and features in the present disclosure can be combined with each other in the case of no conflict.

[0020] It should be noted that the terms “first”, “second”, and the like in the present disclosure are only used to distinguish different devices, modules or units, and are not intended to limit the order or interdependence of the functions performed by these devices, modules or units.

[0021] It should be noted that the terms “one”, “multiple” in the present disclosure are illustrative and not restrictive, and those skilled in the art should understand that “one or more” should be understood unless otherwise explicitly stated in the context.

[0022] The names of the messages or information exchanged between the devices in the embodiments of the present disclosure are only for illustrative purposes and are not intended to limit the scope of the messages or information.

[0023] The present disclosure will be described in detail below with reference to the accompanying drawings and embodiments.

[0024] Figure 1 A flow 100 of some embodiments of the wall material recognition method based on laser point cloud scanning according to the present disclosure is shown. The wall material recognition method based on laser point cloud scanning includes the following steps:

[0025] Step 101, control the shooting device to perform image shooting on the original house wall to generate an original house wall image set.

[0026] In some embodiments, the execution subject of the wall material recognition method based on laser point cloud scanning can control the shooting device to perform image shooting on the original house wall to generate an original house wall image set in a wired manner or a wireless manner. The shooting device can be manually held or fixed by a pre-laid shooting track. Secondly, the shooting device can be controlled to perform image shooting after determining that the shooting device is fixed. Here, all walls of each room in the original house can be shot. For example, the floor, ceiling and walls of the house are shot.

[0027] It should be noted that the above wireless connection manner can include but is not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other now known or future developed wireless connection manners.

[0028] In some optional implementations of some embodiments, the above execution subject controlling the shooting device to perform image shooting on the original house wall to generate an original house wall image set can include the following steps:

[0029] First, control the shooting device to perform image shooting on the original house wall to obtain a house interior image sequence. For each wall, at least one frontally shot image in each house interior image is shot.

[0030] Second, extract wall structure feature points from each house interior image in the above house interior image sequence to generate a wall structure feature point sequence set. The wall structure feature points can be extracted by a pre-set wall structure feature point extraction algorithm to generate a wall structure feature point sequence set. Here, feature points on the corner line of each house interior image can be extracted. Each wall structure feature point sequence can represent a corner line.

[0031] As an example, the wall structure feature point extraction algorithm can include, but is not limited to, at least one of the following: Surf (Speeded Up Robust Features) algorithm, harris corner detection, FAST corner detection, BRIEF (Binary Robust Independent Elementary Features) algorithm, etc.

[0032] Thirdly, according to the wall structure feature point sequence set, a wall image corresponding to each wall in each interior image of the interior image sequence is cropped as a raw house wall image to obtain a raw house wall image set. Firstly, each wall structure feature point in each wall structure feature point sequence can be fitted to obtain a wall corner line. Then, an interior angle between each wall corner line in each interior image can be determined. If the interior angle is less than or greater than a preset angle threshold, it indicates that the interior image is a frontally photographed wall image. Finally, the frontally photographed wall image corresponding to each wall can be determined as a raw house wall image to obtain a raw house wall image set.

[0033] Step 102, wall damage identification is performed on each raw house wall image in the raw house wall image set to generate a wall damage image feature set.

[0034] In some embodiments, the execution subject can perform wall damage identification on each raw house wall image in the raw house wall image set to generate a wall damage image feature set. Each wall damage image feature can correspond to a wall of the raw house.

[0035] In some optional implementations of some embodiments, the execution subject performs wall damage identification on each raw house wall image in the raw house wall image set to generate a wall damage image feature set, which can include the following steps:

[0036] For each raw house wall image in the raw house wall image set, a wall damage image feature is generated by the following steps:

[0037] In practice, considering that the raw house wall has wall abnormal problems, such as water immersion, cracking, etc. Therefore, firstly, color feature detection can be performed on the wall to determine whether there is an abnormality. Then, texture feature extraction is performed to determine whether there is a cracking condition.

[0038] Firstly, color feature detection is performed on the raw house wall image to generate a wall color feature map. The color feature detection can be performed on the raw house wall image by a color histogram algorithm to generate a wall color feature map.

[0039] Secondly, the wall texture feature extraction algorithm is used to extract the wall texture feature, and an initial wall texture feature map is generated. Then, the texture feature in the initial wall texture feature map is filtered by the gray level co-occurrence matrix method, and the texture feature corresponding to the wall cracking state is extracted, so as to obtain the wall texture feature map. Here, the wall texture feature is used to extract the feature of whether the wall is cracked.

[0040] As an example, the wall texture feature algorithm can include but is not limited to at least one of the following: GLCM (Gray Level Co-occurrence Matrix) detection algorithm, Tamura texture analysis method, GWBTF (Gabor wavelet-based texture feature extraction) algorithm, etc.

[0041] Thirdly, the wall roughness anomaly detection is performed on the original house wall image to generate a wall roughness anomaly feature map. The initial wall texture feature map of the original house wall image is subjected to roughness feature extraction by linear discriminant analysis (Linear Discriminant Analysis, LDA), and the wall roughness anomaly feature map is generated.

[0042] Fourthly, according to the wall color feature map, the wall texture feature map and the wall roughness anomaly feature map, the wall damage image feature is generated. The wall color feature map, the wall texture feature map and the wall roughness anomaly feature map are superimposed to obtain the wall damage image feature.

[0043] In practice, image recognition technology based on deep learning, infrared thermal imaging technology, ultrasonic detection technology, and radar scanning technology can each achieve to some extent the assessment of the material and health of the wall. However, each of these alternatives has its own limitations in practical application. For example, image recognition technology based on deep learning is greatly affected by environmental factors such as light and angle; infrared thermal imaging technology mainly relies on the temperature distribution of the wall surface, and has limitations in judging the internal structure of the wall; although ultrasonic detection technology and radar scanning technology can penetrate the wall to detect, they are relatively complex to operate and have high equipment costs. Therefore, in the process of using the above solutions to solve the problems mentioned in the background art, the following technical problem two often occurs: the determination of the material and damage degree of the wall is not accurate enough, and there is a large difference between the rendered image and the actual scene. As a result, rendering resources are wasted. To solve the above technical problem two, the inventors have decided to use the following solution.

[0044] Step 103, controlling a three-dimensional laser scanning device to perform laser scanning on the wall of the house after the demolition of the decoration layer to generate wall point cloud data of the house.

[0045] In some embodiments, the execution subject can control a three-dimensional laser scanning device to perform laser scanning on the wall of the house after the demolition of the decoration layer to generate wall point cloud data of the house. The three-dimensional laser scanning device can be manually operated at a fixed point (e.g., scanning each wall in the middle of the house), or can be deployed on the preset shooting track to perform laser scanning in the house to obtain the wall point cloud data of the house.

[0046] Step 104, performing point cloud file analysis on the wall point cloud data of the house to generate a wall point cloud data set of the house.

[0047] In some embodiments, the execution subject can perform point cloud file analysis on the wall point cloud data of the house to generate a wall point cloud data set of the house. Each wall point cloud data of the house can correspond to one wall of the house after the demolition of the decoration layer.

[0048] In some optional implementations of some embodiments, the execution subject performing point cloud file analysis on the wall point cloud data of the house to generate a wall point cloud data set of the house can include the following steps:

[0049] First, data preprocessing is performed on the wall point cloud data of the house to generate processed wall point cloud data. The data preprocessing can be to remove the point cloud data outside the wall of the house to obtain the processed wall point cloud data.

[0050] Secondly, the wall point cloud data after the above processing is filtered to generate filtered wall point cloud data. The filtering can be performed by a non-local mean filtering method to remove point cloud data that is irrelevant to the house, such as data caused by light, people moving, dust, etc., to obtain filtered wall point cloud data.

[0051] Thirdly, the filtered wall point cloud data is subjected to structured feature extraction to generate a set of structured point cloud coordinate sequences. The filtered wall point cloud data can be subjected to structured feature extraction by a point cloud structuring algorithm to generate a set of structured point cloud coordinate sequences. The structured feature extraction can be extracting point cloud coordinates representing the edges of the wall as the structured point cloud coordinate sequences. Each wall edge can correspond to a structured point cloud coordinate sequence.

[0052] As an example, the point cloud structuring algorithm can include, but is not limited to, at least one of the following: NARF (normal aligned radial feature) algorithm, ISS (Intrinsic Shape Signatures), a key point extraction method based on the intrinsic shape features of point cloud, SUSAN (Smallest Univalue Segment Assimilating Nucleus), a corner detection algorithm based on image processing, etc.

[0053] Fourthly, the wall point cloud data is subjected to point cloud data grouping processing according to the connection lines of the structured point cloud coordinates in the set of structured point cloud coordinate sequences to generate a set of wall point cloud data of the house. The point cloud data can be divided according to the connection lines of the structured point cloud coordinates to obtain the wall point cloud data of the house.

[0054] In step 105, the wall material of each wall surface of the original house after the demolition and decoration layer is identified according to the set of wall damage image features and the set of wall point cloud data of the house to generate a set of wall material identification information, and the set of wall material identification information is stored.

[0055] In some embodiments, the execution subject can identify the wall material of each wall surface of the original house after the demolition and decoration layer according to the set of wall damage image features and the set of wall point cloud data of the house to generate a set of wall material identification information, and store the set of wall material identification information. Each wall material information in the set of wall material information can include, but is not limited to, at least one of the following: wall material data, wall age data, and wall integrity data. The wall integrity data can be used to represent the integrity of each coordinate point on the wall.

[0056] In some optional implementations of some embodiments, the execution subject performs wall material identification on each wall after the original house demolition and decoration layer according to the wall damage image feature set and the house wall point cloud data set to generate a wall material identification information set, which can include the following steps:

[0057] Firstly, the wall damage image feature set and the house wall point cloud feature set are matched to determine the wall damage image feature and the house wall point cloud data corresponding to the same wall. Each wall damage image feature and each house wall point cloud feature can be provided with a corresponding wall number. Thus, the same wall can be matched by the number to determine the wall damage image feature and the house wall point cloud data corresponding to the same wall.

[0058] Secondly, for the wall damage image feature and the house wall point cloud data corresponding to the same wall, the following steps are used to generate the wall material identification information of each wall after the original house demolition and decoration layer:

[0059] Step one, the wall roughness is divided by using the house wall point cloud data to generate a wall roughness point cloud feature map. The roughness of the wall can be distinguished by the error of the house wall point cloud data, and different colors can be used to represent it. Specifically, the standard deviation of the point cloud coordinates of the house wall point cloud data corresponding to a wall can be determined as the error of the house wall point cloud data to represent the roughness of the wall. Here, only the standard deviation of the coordinates in the normal direction of the wall (for example, the horizontal direction) can be determined.

[0060] Step two, the wall color feature of the house wall point cloud data is extracted to generate a wall color point cloud feature map. Firstly, the house wall point cloud data can be read by a pre-set software library. Then, the color feature is extracted by the color feature extraction function in the software library. Finally, the color value corresponding to each coordinate is determined to obtain the wall color point cloud feature map.

[0061] As an example, the software library can include but is not limited to at least one of the following: liblas: an open source library for processing laser radar data. The color feature extraction function in the software library can be the GetColor() function.

[0062] Step three, according to the wall roughness point cloud feature map and the wall color point cloud feature map, the material feature information of the wall is determined. In practice, different wall materials and different flatness will be distinguished by different colors, for example, deep red for the area with large angle defects and concave of the ceramic or block wall, and light gray for the flat concrete wall. Therefore, the wall roughness point cloud feature map and the wall color point cloud feature map can be input into the pre-trained decision tree model to generate the wall material feature information.

[0063] Step four, the material feature information and the wall damage image feature are fused to generate a wall damage feature map, and the wall damage feature map is determined as the wall material recognition information. The feature fusion is feature superposition. For example, the material feature and the wall damage feature corresponding to the same coordinate are bound to the coordinate to obtain the wall damage feature map.

[0064] Optionally, the execution subject can further perform the following steps:

[0065] First step, according to the house wall point cloud feature set, a three-dimensional structure diagram of the house is constructed. The house wall point cloud feature set can be input into a pre-set point cloud processing software to construct a three-dimensional structure diagram of the house in a point cloud coordinate system.

[0066] As an example, the point cloud processing software can include but is not limited to at least one of the following: CloudCompare, MeshLab, etc.

[0067] Second step, the volume material data, wall age data, and wall integrity data included in the wall material recognition information that does not meet the pre-set condition are rendered into the three-dimensional structure diagram of the house to generate a rendered three-dimensional structure diagram of the house. The actual features of the wall can be rendered in the three-dimensional structure diagram of the house according to the wall damage feature map in the wall material recognition information to obtain the rendered three-dimensional structure diagram of the house.

[0068] As an example, the rendered three-dimensional structure diagram of the house can be as shown in Figure 2 As an example, the rendered three-dimensional structure diagram of the house can be as shown in

[0069] Thirdly, the rendered three-dimensional structure diagram of the house is stored and sent to a target display terminal for display.

[0070] Optionally, the execution subject constructs a three-dimensional structure diagram of the house according to the house wall point cloud feature set, which can include the following steps:

[0071] Firstly, in the coordinate system of the three-dimensional laser scanning device, each structured point cloud coordinate in the structured point cloud coordinate sequence is connected as a wall segmentation line.

[0072] Secondly, in the coordinate system, each wall segmentation line is taken as a wall structure, and the point cloud coordinates of the house wall point cloud data corresponding to each wall are filled as wall surface features to obtain a three-dimensional structure diagram of the house.

[0073] The steps 103-105 and their related contents are an invention point of an embodiment of the present disclosure, which solves the second technical problem mentioned in the background that the determination of the wall material and the damage degree is not accurate enough, and there is a large difference between the rendered image and the actual scene. Therefore, rendering resources are wasted. The factors that lead to the waste of rendering resources are often as follows: the determination of the wall material and the damage degree is not accurate enough, and there is a large difference between the rendered image and the actual scene. In order to achieve this effect, first, improve the detection accuracy and efficiency: compared with the traditional manual detection method and the traditional detection instrument method, the three-dimensional information of the wall can be quickly and accurately obtained by using the laser point cloud scanning technology, so as to realize the accurate determination of the wall material and the health degree. This not only greatly improves the detection accuracy, but also significantly shortens the detection time and improves the work efficiency. Secondly, comprehensiveness and depth: the technical scheme can comprehensively cover every corner of the wall and provide detailed three-dimensional point cloud data, so that the wall material determination and health degree evaluation are more comprehensive and in-depth. In comparison, the present scheme can reveal more detailed information about the wall, providing stronger support for subsequent decoration and maintenance. Then, intelligence and automation: through the above-mentioned related processing technologies of point cloud features and image features, the intelligence and automation of wall material identification can be realized. This not only reduces the influence of human factors on the detection results, but also makes the detection process more convenient and efficient. It has obvious advantages in the level of intelligence. Finally, reduce the cost and improve the competitiveness: since the technical scheme can significantly improve the detection efficiency and accuracy, it can reduce the detection cost and improve the competitiveness of the product. At the same time, by providing comprehensive and accurate wall information, it can further help customers better plan the decoration and maintenance scheme, further improve the customer's satisfaction and loyalty. Finally, the accuracy of the house material information identification is improved. Further, the consumption of rendering resources is reduced.

[0074] In addition, in contrast, the technical scheme of "wall material information identification based on laser point cloud scanning" has the advantages of high precision, high efficiency, comprehensiveness, and intelligent level, and becomes a better choice to achieve the purpose of the invention. This technical scheme can fully cover every corner of the wall, provide detailed three-dimensional point cloud data, and make the wall material determination and health degree evaluation more accurate and efficient. Therefore, our technical scheme can provide users with better and more efficient service experience and meet the diverse needs of customers.

[0075] The above various embodiments of the present disclosure have the following beneficial effects: The wall material identification method based on laser point cloud scanning of some embodiments of the present disclosure can reduce the storage resource occupation of the generated wall material identification information. Specifically, the reason for occupying more storage resources is that the artificial detection method is greatly affected by human factors, has low accuracy and low efficiency. And although the accuracy of the traditional detection instrument method is improved, it still needs manual operation, and it is difficult to fully and deeply obtain the three-dimensional information and internal condition of the wall. Therefore, multiple measurements are needed to ensure accuracy. Based on this, the wall material identification method based on laser point cloud scanning of some embodiments of the present disclosure first considers that the original house wall is prone to abnormal conditions. Therefore, by controlling the shooting device, image shooting is performed on the original house wall to generate an original house wall image set. Thus, wall damage identification can be performed from the appearance of the original wall without removing the decoration layer. Thereby, a wall damage image feature set can be generated. Then, considering the house wall after removing the decoration layer, even if the surface image can be shot, the internal condition is difficult to detect. Therefore, by controlling the three-dimensional laser scanning device, laser scanning is performed on the house wall after removing the decoration layer to generate house wall point cloud data. Then, the house wall point cloud data can be analyzed to generate a house wall point cloud data set. Thereby, the wall damage image features and the house wall point cloud data corresponding to each wall can be determined. Thus, the material information of the wall can be comprehensively judged through wall material identification, and the accuracy of the generated material information can be improved. Finally, the wall material identification information set can be stored. Here, each wall material information can include wall material data, wall age data, and wall integrity data. Furthermore, not only the accuracy of identification can be determined, but also the diversity of wall material identification information can be improved. Thus, multiple measurements and storage of the results of multiple measurements can be avoided. Furthermore, the occupation of computing resources can be reduced.

[0076] Further reference Figure 3 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a wall material identification device based on laser point cloud scanning, which device embodiments correspond to the method embodiments shown in Figure 1 The device can be applied in various electronic devices.

[0077] As Figure 3 shown, the wall material recognition device 300 based on laser point cloud scanning of some embodiments includes an image shooting unit 301, a wall damage recognition unit 302, a laser scanning unit 303, a file analysis unit 304, and a wall material recognition unit 305. Among them, the image shooting unit 301 is configured to control the shooting device to shoot the original house wall to generate a set of original house wall images; the wall damage recognition unit 302 is configured to recognize the wall damage of each original house wall image in the set of original house wall images to generate a set of wall damage image features, wherein each wall damage image feature corresponds to a wall of the original house; the laser scanning unit 303 is configured to control the three-dimensional laser scanning device to scan the house wall after the decoration layer is removed to generate the house wall point cloud data; the file analysis unit 304 is configured to analyze the house wall point cloud data to generate a set of house wall point cloud data, wherein each house wall point cloud feature corresponds to a wall of the original house after the decoration layer is removed; the wall material recognition unit 305 is configured to recognize the wall material of each wall of the original house after the decoration layer is removed according to the set of wall damage image features and the set of house wall point cloud data to generate a set of wall material recognition information, and store the set of wall material recognition information, wherein each wall material information in the set of wall material information includes at least one of the following: wall material data, wall age data, and wall integrity data.

[0078] It can be understood that the units described in the device 300 correspond to the respective steps in the method described with reference to Figure 1 Thus, the operations, features and advantages described above for the method also apply to the device 300 and the units contained therein, which will not be described here again.

[0079] Reference is made below to Figure 4 which shows a structural schematic diagram of an electronic device (e.g., a computing device) 400 suitable for implementing some embodiments of the present disclosure. Figure 4 The electronic device shown is only an example and should not impose any limitation on the function and use range of the embodiments of the present disclosure.

[0080] As Figure 4As shown, the electronic device 400 can include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 401 that can perform various appropriate actions and processes according to programs stored in a read-only memory 402 or loaded from a storage device 408 into a random access memory 403. Various programs and data required for the operation of the electronic device 400 are also stored in the random access memory 403. The processing device 401, the read-only memory 402, and the random access memory 403 are connected to each other through a bus 404. An input / output interface 405 is also connected to the bus 404.

[0081] Generally, the following devices can be connected to the I / O interface 405: input devices 406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 408 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 409. The communication devices 409 can allow the electronic device 400 to communicate with other devices wirelessly or wired to exchange data. Although Figure 4 The electronic device 400 is shown with various devices, but it should be understood that all of the illustrated devices are not required, and more or fewer devices can alternatively be implemented. Figure 4 Each block shown in the flowcharts can represent a device, or multiple devices, as necessary.

[0082] In particular, processes described above with reference to the flowcharts can be implemented as a computer software program according to some embodiments of the present disclosure. For example, some embodiments of the present disclosure include a computer program product including a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In some such embodiments, the computer program can be downloaded and installed from a network through the communication devices 409, or installed from the storage devices 408, or installed from the read-only memory 402. When the computer program is executed by the processing device 401, the above-described functions defined in the methods of some embodiments of the present disclosure are performed.

[0083] Note that the computer readable medium in some embodiments of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination thereof. The computer readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination of the foregoing. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program used by an instruction execution system, apparatus or device, or that can be used by or in connection with an instruction execution system, apparatus or device. In some embodiments of the present disclosure, the computer readable signal medium can include a computer readable program code propagated in or on a carrier medium, in which the computer readable program code is embodied. Such propagated computer readable program code can take many forms, including but not limited to, an electromagnetic signal, an optical signal or any suitable combination of the foregoing. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate or transport a program for use by or in connection with an instruction execution system, apparatus or device. Program code embodied on a computer readable medium can be transmitted using any suitable medium, including but not limited to, wire, cable, wireless, RF, infrared or any suitable combination of the foregoing.

[0084] In some embodiments, the client, server, or both can communicate using any current known or future developed network protocol, such as HTTP (Hyper Text Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), the Internet, and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any current known or future developed networks.

[0085] The computer readable medium can be included in the electronic device, or can exist separately from the electronic device. The computer readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: control a photographing device to perform image photographing on original house wall bodies to generate a set of original house wall body images; perform wall damage identification on each original house wall body image in the set of original house wall body images to generate a set of wall damage image features, wherein each wall damage image feature corresponds to a wall body of the original house; control a three-dimensional laser scanning device to perform laser scanning on the house wall bodies after the decoration layer is removed to generate house wall body point cloud data; perform point cloud file analysis on the house wall body point cloud data to generate a set of house wall body point cloud data, wherein each house wall body point cloud data corresponds to a wall body of the original house after the decoration layer is removed; perform wall material identification on each wall body of the original house after the decoration layer is removed according to the set of wall damage image features and the set of house wall body point cloud data to generate a set of wall material identification information, and store the set of wall material identification information, wherein each wall material information in the set of wall material information includes at least one of the following: wall material data, wall age data, and wall integrity data.

[0086] Computer program code for carrying out operations of some embodiments of the disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0087] The computer program product of the first aspect can include a computer readable storage medium. The computer readable storage medium can include instructions. The instructions can include one or both of: instructions for causing a computer to implement a method as described above; and instructions for causing a computer to operate based on a system as described above. The computer readable storage medium can include one or more of: a magnetic disk; a magnetic tape; a magneto-optical disk; a semiconductor memory (e.g., a RAM, a ROM, a flash memory, etc.); and an optical disk.

[0088] The units described in some embodiments of the present disclosure can be implemented in the form of software, or can be implemented in the form of hardware. The described units can also be arranged in a processor, for example, can be described as: a processor including an image shooting unit, a wall damage identification unit, a laser scanning unit, a file analysis unit, and a wall material identification unit. Among them, the name of these units does not constitute a limitation to the unit itself in some cases, for example, the laser scanning unit can also be described as: a unit for controlling a three-dimensional laser scanning device, and performing laser scanning on the wall of the house after the demolition decoration layer is removed.

[0089] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, example types of hardware logic components that can be used include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), etc.

[0090] The above description is merely some of the preferred embodiments of the present disclosure and a description of the principles of the technology used. Those skilled in the art should understand that the scope of the application involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combinations of the above technical features or equivalent features without departing from the above inventive concept. For example, the above features are replaced with the technical features disclosed in the embodiments of the present disclosure (but not limited to) having similar functions to form technical solutions.

Claims

1. A wall material identification method based on laser point cloud scanning, comprising: controlling a photographing device to perform image shooting on original house walls to generate a set of original house wall images; performing wall damage identification on each original house wall image in the set of original house wall images to generate a set of wall damage image features, wherein each wall damage image feature corresponds to a wall of the original house; controlling a three-dimensional laser scanning device to perform laser scanning on the walls of the house after removing the decoration layer to generate house wall point cloud data; performing point cloud file analysis on the house wall point cloud data to generate a set of house wall point cloud data, wherein each house wall point cloud data corresponds to a wall of the original house after removing the decoration layer; performing wall material identification on each wall of the original house after removing the decoration layer according to the set of wall damage image features and the set of house wall point cloud data to generate a set of wall material identification information, and storing the set of wall material identification information, wherein each wall material information in the set of wall material information includes at least one of the following: wall material data, wall age data, and wall integrity data, comprising: performing matching processing on the set of wall damage image features and the set of house wall point cloud data to determine wall damage image features and house wall point cloud data corresponding to the same wall; for wall damage image features and house wall point cloud data corresponding to the same wall, generating wall material identification information corresponding to each wall of the original house after removing the decoration layer by the following steps: dividing the wall into roughness according to the house wall point cloud data to generate a wall roughness point cloud feature map; extracting wall color features from the house wall point cloud data to generate a wall color point cloud feature map; determining material feature information of the wall according to the wall roughness point cloud feature map and the wall color point cloud feature map; performing feature fusion on the material feature information and the wall damage image features to generate a wall damage feature map, and determining the wall damage feature map as the wall material identification information.

2. The method of claim 1, wherein, The method further comprises: constructing a house three-dimensional structure map according to the set of house wall point cloud data; rendering body material data, wall age data, and wall integrity data included in wall material identification information in the set of wall material identification information that does not meet a preset condition to the house three-dimensional structure map to generate a rendered house three-dimensional structure map; storing the rendered house three-dimensional structure map and sending it to a target display terminal for display.

3. The method of claim 1, wherein, The control of the photographing device to perform image shooting on the original house walls to generate a set of original house wall images comprises: controlling the photographing device to perform image shooting on the original house walls to obtain a sequence of house interior images; extracting wall structure feature points from each house interior image in the sequence of house interior images to generate a set of wall structure feature point sequences; According to the wall structure feature point sequence set, a wall image corresponding to each wall of the house is cropped from each house interior image in the house interior image sequence as an original house wall image, so as to obtain an original house wall image set.

4. The method of claim 1, wherein, The wall damage identification on each original house wall image in the original house wall image set comprises: For each original house wall image in the original house wall image set, a wall damage image feature is generated through the following steps: color feature detection is performed on the original house wall image to generate a wall color feature map; wall texture feature extraction is performed on the original house wall image to generate a wall texture feature map; wall roughness anomaly detection is performed on the original house wall image to generate a wall roughness anomaly feature map; a wall damage image feature is generated according to the wall color feature map, the wall texture feature map and the wall roughness anomaly feature map.

5. The method of claim 2, wherein, The point cloud file analysis on the house wall point cloud data comprises: data preprocessing is performed on the house wall point cloud data to generate processed wall point cloud data; filtering processing is performed on the processed wall point cloud data to generate filtered wall point cloud data; structured feature extraction is performed on the filtered wall point cloud data to generate a structured point cloud coordinate sequence set; point cloud data grouping processing is performed on the house wall point cloud data according to the connection line of each structured point cloud coordinate in the structured point cloud coordinate sequence set to generate a house wall point cloud data set.

6. The method of claim 5, wherein, The construction of the house three-dimensional structure map according to the house wall point cloud data set comprises: in the coordinate system of the three-dimensional laser scanning device, each structured point cloud coordinate in the structured point cloud coordinate sequence is connected as a wall segmentation line; in the coordinate system, each wall segmentation line is taken as a wall structure, and the point cloud coordinates of the house wall point cloud data corresponding to each wall are filled as a wall surface feature to obtain a house three-dimensional structure map.

7. A wall material identification device based on laser point cloud scanning, comprising: an image shooting unit configured to control a shooting device to shoot images of original house walls to generate an original house wall image set; a wall damage identification unit configured to identify wall damage of each original house wall image in the original house wall image set to generate a wall damage image feature set, wherein each wall damage image feature corresponds to a wall of the original house; a laser scanning unit configured to control a three-dimensional laser scanning device to perform laser scanning on the house walls after the demolition of the decoration layer to generate house wall point cloud data; a file analysis unit configured to perform point cloud file analysis on the house wall point cloud data to generate a house wall point cloud data set, wherein each house wall point cloud feature corresponds to a wall of the original house after the demolition of the decoration layer; The wall material recognition unit is configured to perform wall material recognition on each wall surface after the original house demolition and decoration layer according to the wall damage image feature set and the house wall point cloud data set, to generate a wall material recognition information set, and to store the wall material recognition information set, wherein each wall material information in the wall material information set includes at least one of the following: wall material data, wall age data, and wall integrity data, including: performing matching processing on the wall damage image feature set and the house wall point cloud data set to determine the wall damage image feature and the house wall point cloud data corresponding to the same wall surface; for the wall damage image feature and the house wall point cloud data corresponding to the same wall surface, the wall material recognition information of each wall surface after the original house demolition and decoration layer is generated by the following steps: performing roughness division on the wall using the house wall point cloud data to generate a wall roughness point cloud feature map; extracting wall color features from the house wall point cloud data to generate a wall color point cloud feature map; determining the material feature information of the wall according to the wall roughness point cloud feature map and the wall color point cloud feature map; performing feature fusion on the material feature information and the wall damage image feature to generate a wall damage feature map, and determining the wall damage feature map as the wall material recognition information.

8. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1-6.

9. A computer readable medium having stored thereon a computer program, wherein, The computer program is executed by the processor to implement the method of any one of claims 1-6.

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