Planar structure chart processing method and device

Abstract

The embodiment of the invention discloses a plane structure chart processing method and device, and the method comprises the steps: determining a plane structure chart comprising a plurality of boundary lines based on original point cloud data corresponding to a target space; obtaining an in-space point cloud set and an out-space point cloud set according to related information of point clouds in the original point cloud data; the in-space point cloud set comprises at least one in-space point cloud, and the out-space point cloud set comprises at least one out-space point cloud; and adjusting the position of at least one boundary line in the plane structure chart based on the point cloud set in the space and the point cloud set outside the space to obtain the adjusted plane structure chart.

Description

Technical Field

[0001] The present disclosure relates to Internet technology, and in particular to a method and device for processing a planar structure diagram. Background Art

[0002] Computer-aided design (CAD) uses computer technology and specific software to create, modify and optimize design drawings. Compared with traditional manual drawing, CAD drawing is more efficient and accurate. With CAD drawing, designers and engineers can generate design drawings more quickly and accurately measure and analyze the design. CAD drawing can be used in many fields, especially in the field of architectural design, allowing architects to transform design ideas into specific floor plans and three-dimensional models. Summary of the invention

[0003] Embodiments of the present disclosure provide a method and device for processing a planar structure graph.

[0004] According to one aspect of an embodiment of the present disclosure, a method for processing a planar structure graph is provided, comprising:

[0005] Based on the original point cloud data corresponding to the target space, a plane structure diagram including a plurality of boundary lines is determined;

[0006] According to the relevant information of the point cloud in the original point cloud data, a point cloud set in space and a point cloud set outside space are obtained; the point cloud set in space includes at least one point cloud in space, and the point cloud set outside space includes at least one point cloud outside space;

[0007] Based on the point cloud set within the space and the point cloud set outside the space, the position of at least one boundary line in the plane structure diagram is adjusted to obtain the adjusted plane structure diagram.

[0008] Optionally, obtaining the point cloud set in space and the point cloud set outside space according to the relevant information of the point cloud in the original point cloud data includes:

[0009] Determining relevant information of at least one point cloud in the original point cloud data;

[0010] Determine the category of the point cloud according to the relevant information of the point cloud; the category of the point cloud includes the point cloud within the space and the point cloud outside the space;

[0011] Based on the category of at least one point cloud in the original point cloud data, the in-space point cloud set and the out-space point cloud set are obtained.

[0012] Optionally, determining a plane structure diagram including a plurality of boundary lines based on the original point cloud data corresponding to the target space includes:

[0013] Performing semantic recognition on the original point cloud data to obtain a boundary point cloud set whose semantic recognition result is a boundary line;

[0014] Based on the boundary point cloud set, the positions of the plurality of boundary lines are determined to obtain the plane structure diagram.

[0015] Optionally, it also includes:

[0016] Determining point cloud intensity information and display difference information corresponding to at least one point cloud in the original point cloud data;

[0017] According to the point cloud intensity information and the display difference information of the at least one point cloud, a position in the plane structure diagram connected to the outside of the space is determined.

[0018] Optionally, before performing semantic recognition on the original point cloud data to obtain a boundary point cloud set whose semantic recognition result is a boundary line, the method further includes:

[0019] Determine a point cloud density corresponding to at least one point cloud in the original point cloud data, and perform denoising processing on the original point cloud data according to the point cloud density to obtain denoised point cloud data;

[0020] The performing semantic recognition on the original point cloud data to obtain a boundary point cloud set whose semantic recognition result is a boundary line includes:

[0021] Semantic recognition is performed on the denoised point cloud data to obtain a boundary point cloud set whose semantic recognition result is a boundary line.

[0022] Optionally, it also includes:

[0023] Determine a colored point cloud image corresponding to the target space based on the original point cloud data and at least one image corresponding to the target space;

[0024] Determine a transformation matrix between the colored point cloud image and the plane structure image based on the original point cloud data;

[0025] Based on the transformation matrix, the colored point cloud image and the plane structure image are displayed on the same display interface.

[0026] Optionally, it also includes:

[0027] In response to receiving at least one operation instruction through the plane structure diagram, performing corresponding operation processing on the plane structure diagram according to the operation instruction to obtain a processing structure diagram;

[0028] The processing structure diagram is displayed in the display interface, and the first operation area corresponding to the operation instruction is highlighted in the colored point cloud diagram.

[0029] Optionally, the operation instruction includes a height adjustment instruction and / or a position adjustment instruction;

[0030] The performing corresponding operation processing on the plane structure diagram according to the operation instruction to obtain the processing structure diagram includes:

[0031] According to the position and adjustment value corresponding to the height adjustment instruction, the height of the corresponding position in the plane structure diagram is adjusted so that the adjusted height value corresponds to the adjustment value, thereby obtaining the processing structure diagram; and / or,

[0032] According to the adjustment object, adjustment direction and adjustment distance corresponding to the position adjustment instruction, the adjustment object is adjusted to the adjustment direction by the adjustment distance to obtain the processing structure diagram.

[0033] Optionally, highlighting a first operation area corresponding to the operation instruction in the colored point cloud image includes:

[0034] Determine a second operation area of ​​the operation instruction in the plane structure diagram based on a difference between the processing structure diagram and the plane structure diagram;

[0035] Based on the transformation matrix and the second operation area, a first operation area in the shaded point cloud image is determined.

[0036] According to another aspect of an embodiment of the present disclosure, there is provided a planar structure graph processing device, including:

[0037] A structure diagram determination module, used to determine a plane structure diagram including a plurality of boundary lines based on original point cloud data corresponding to the target space;

[0038] A point cloud classification module, used to obtain a point cloud set in space and a point cloud set outside space according to relevant information of the point cloud in the original point cloud data; the point cloud set in space includes at least one point cloud in space, and the point cloud set outside space includes at least one point cloud outside space;

[0039] The structure diagram adjustment module is used to adjust the position of at least one boundary line in the plane structure diagram based on the point cloud set in the space and the point cloud set outside the space to obtain the adjusted plane structure diagram.

[0040] Optionally, the point cloud classification module includes:

[0041] A point cloud category determination unit, used to determine relevant information of at least one point cloud in the original point cloud data; determine the category of the point cloud according to the relevant information of the point cloud; the category of the point cloud includes the point cloud within the space and the point cloud outside the space;

[0042] The point cloud collection unit is used to obtain the point cloud collection within the space and the point cloud collection outside the space based on the category of at least one point cloud in the original point cloud data.

[0043] Optionally, the structure diagram determining module includes:

[0044] A semantic recognition unit, used for performing semantic recognition on the original point cloud data, and obtaining a boundary point cloud set whose semantic recognition result is a boundary line;

[0045] The boundary determination unit is used to determine the positions of the plurality of boundary lines based on the boundary point cloud set to obtain the planar structure diagram.

[0046] Optionally, the structure diagram determining module further includes:

[0047] A connected position determination unit is used to determine the point cloud strength information and display difference information corresponding to at least one point cloud in the original point cloud data; based on the point cloud strength information and display difference information of the at least one point cloud, determine the position in the plane structure diagram that is connected to the outside of the space.

[0048] Optionally, the structure diagram determining module further includes:

[0049] a denoising unit, configured to determine a point cloud density corresponding to at least one point cloud in the original point cloud data, and to perform denoising processing on the original point cloud data according to the point cloud density to obtain denoised point cloud data;

[0050] The semantic recognition unit is used to perform semantic recognition on the denoised point cloud data to obtain a boundary point cloud set whose semantic recognition result is a boundary line.

[0051] Optionally, the device further comprises:

[0052] A point cloud coloring module, used to determine a colored point cloud image corresponding to the target space based on the original point cloud data and at least one image corresponding to the target space;

[0053] The image display module is used to determine the transformation matrix between the colored point cloud image and the plane structure image based on the original point cloud data; based on the transformation matrix, the colored point cloud image and the plane structure image are displayed on the same display interface.

[0054] Optionally, the device further comprises:

[0055] A structure diagram operation module, configured to, in response to receiving at least one operation instruction through the plane structure diagram, perform corresponding operation processing on the plane structure diagram according to the operation instruction to obtain a processing structure diagram;

[0056] A synchronous display module is used to display the processing structure diagram in the display interface and highlight the first operation area corresponding to the operation instruction in the colored point cloud diagram.

[0057] Optionally, the operation instruction includes a height adjustment instruction and / or a position adjustment instruction;

[0058] The structure diagram operation module is specifically used to adjust the height of the corresponding position in the plane structure diagram according to the position and adjustment value corresponding to the height adjustment instruction, so that the adjusted height value corresponds to the adjustment value, and obtain the processing structure diagram; and / or, according to the adjustment object, adjustment direction and adjustment distance corresponding to the position adjustment instruction, adjust the adjustment distance of the adjustment object toward the adjustment direction to obtain the processing structure diagram.

[0059] Optionally, the synchronous display module is specifically used to determine the second operation area of ​​the operation instruction in the plane structure diagram based on the difference between the processing structure diagram and the plane structure diagram; based on the transformation matrix and the second operation area, determine the first operation area in the shaded point cloud diagram.

[0060] According to another aspect of the present disclosure, there is provided an electronic device, including:

[0061] A memory for storing a computer program product;

[0062] The processor is used to execute the computer program product stored in the memory, and when the computer program product is executed, the planar structure diagram processing method described in any of the above embodiments is implemented.

[0063] According to another aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the planar structure diagram processing method described in any of the above embodiments is implemented.

[0064] According to another aspect of the embodiments of the present disclosure, a computer program product is provided, including computer program instructions, which, when executed by a processor, implement the planar structure graph processing method described in any of the above embodiments.

[0065] The method and device for processing a plane structure diagram provided based on the above-mentioned embodiment of the present disclosure include: determining a plane structure diagram including a plurality of boundary lines based on the original point cloud data corresponding to the target space; obtaining a set of point clouds within the space and a set of point clouds outside the space according to the relevant information of the point clouds in the original point cloud data; the set of point clouds within the space includes at least one point cloud within the space, and the set of point clouds outside the space includes at least one point cloud outside the space; based on the set of point clouds within the space and the set of point clouds outside the space, adjusting the position of at least one boundary line in the plane structure diagram to obtain the adjusted plane structure diagram. The embodiment of the present disclosure realizes intelligent distinction of point clouds by determining a set of point clouds within the space and a set of point clouds outside the space, and adjusts the position of at least one boundary line in the plane structure diagram by using the set of point clouds within the space and the set of point clouds outside the space, so that the boundary lines in the plane structure diagram are adjusted to more accurate positions, thereby avoiding errors caused by data confusion.

[0066] The technical solution of the present disclosure is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The accompanying drawings, which constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0068] The present disclosure may be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0069] Figure 1 It is a flowchart of a method for processing a planar structure diagram provided by an exemplary embodiment of the present disclosure;

[0070] Figure 2 This disclosure Figure 1 A schematic flow chart of step 102 in the embodiment shown;

[0071] Figure 3 This disclosure Figure 1 A schematic flow chart of step 104 in the embodiment shown;

[0072] Figure 4 is a flow chart of a method for processing a planar structure diagram provided by another exemplary embodiment of the present disclosure;

[0073] Figure 5 is a schematic diagram of a display interface in a method for processing a planar structure diagram provided by an exemplary embodiment of the present disclosure;

[0074] Figure 6 is a schematic structural diagram of a planar structure graph processing device provided by an exemplary embodiment of the present disclosure;

[0075] Figure 7A block diagram of an electronic device according to an embodiment of the present disclosure is illustrated. DETAILED DESCRIPTION

[0076] Below, the exemplary embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments of the present disclosure, and it should be understood that the present disclosure is not limited to the exemplary embodiments described here.

[0077] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

[0078] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0079] It should also be understood that in the embodiments of the present disclosure, “plurality” may refer to two or more than two, and “at least one” may refer to one, two, or more than two.

[0080] It should also be understood that any component, data or structure mentioned in the embodiments of the present disclosure can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0081] In addition, the term "and / or" in this disclosure is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this disclosure generally indicates that the previous and next associated objects are in an "or" relationship. The data referred to in this disclosure may include unstructured data such as text, images, and videos, and may also be structured data.

[0082] It should also be understood that the description of the various embodiments in the present disclosure focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0083] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0084] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0085] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0086] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0087] The disclosed embodiments can be applied to electronic devices such as terminal devices, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0088] Electronic devices such as terminal devices, computer systems, servers, etc. can be described in the general context of computer system executable instructions (such as program modules) executed by computer systems. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0089] Application Overview

[0090] In the process of realizing the present disclosure, the inventor found that in the decoration business, measuring the house is the most important part, but its labor cost is high, human errors cannot be effectively controlled, and it is difficult to form a standardized operation process. The existing technology uses a laser acquisition device to walk through a room, collect point cloud data in the room, and then use an AI algorithm to automatically generate a structural CAD drawing of the house, but the results output by the AI ​​algorithm may have errors.

[0091] Exemplary Methods

[0092] Figure 1 FIG. 1 is a flow chart of a method for processing a planar structure diagram provided by an exemplary embodiment of the present disclosure. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the following steps are included:

[0093] Step 102: determining a plane structure diagram including a plurality of boundary lines based on the original point cloud data corresponding to the target space.

[0094] Optionally, the plane structure diagram may be a CAD structure diagram. In this embodiment, the plane structure diagram may be obtained by using a method of generating a plane structure diagram from point cloud data that can be realized in the prior art, for example, the original point cloud data is converted to a top-down scene of the target space through a coordinate system conversion, and then coordinate processing is performed (for example, all z-axis coordinates are assigned to 0) to obtain a plane structure diagram in a top-down scene, and the boundary of the target space (for example, a wall, etc.) and at least one structure included in the space (for example, a door, a window, etc.) are displayed in the plane structure diagram.

[0095] The target space in this embodiment may be any space including a boundary, such as a room, a vehicle, etc. For example, when the target space is a room, the original point cloud data is obtained by collecting a three-dimensional point cloud through a laser collection device at at least one point in the room.

[0096] Step 104 , obtaining a point cloud set inside the space and a point cloud set outside the space according to relevant information of the point cloud in the original point cloud data.

[0097] The in-space point cloud set includes at least one in-space point cloud, and the out-space point cloud set includes at least one out-space point cloud. In this embodiment, the original point cloud data is collected in the target space, and the target space may have at least one light-transmitting place of a transparent material (for example, a window, etc.), through which point clouds outside the target space can also be collected in the target space. Therefore, this embodiment classifies the original point cloud data into an in-space point cloud set and an out-space point cloud set.

[0098] Optionally, each point cloud in the original point cloud data includes three-dimensional space coordinates, corresponding point information and reflection intensity information. Optionally, the relevant information of the point cloud may include but is not limited to at least one of the following: point cloud reflection intensity, point cloud distribution characteristics, and point cloud trajectory information. Among them, the point cloud reflection intensity is the reflection intensity value returned by each point during the scanning process of the laser radar during acquisition. This value is related to factors such as the surface material, color, and roughness of the object. This embodiment mainly reflects that different materials have different reflection intensities, because the point cloud outside the space collected in the target space is usually obtained through transparent materials (for example, glass, etc.), and the transparent material is different from the boundary material (wall), and the corresponding reflection intensity is quite different. The point cloud distribution characteristics correspond to the point cloud density. The density of the point cloud in the target space collected in the target space is usually large and evenly distributed, while the density of the point cloud collected in the target space through transparent materials is usually small and discretely distributed. The point cloud trajectory information is determined based on the spatial relationship between adjacent points of the point cloud. The point cloud trajectory information can be determined by the number and distance of adjacent points in each point cloud period. The trajectory information of the point cloud in the target space is relatively similar, while the trajectory information of the point cloud outside the target space is quite different from that of the point cloud in the target space.

[0099] In some embodiments, the point clouds are classified according to relevant information of the point clouds, and the point clouds in the original point cloud data are classified as point clouds within the space or point clouds outside the space.

[0100] Step 106: Based on the point cloud set inside the space and the point cloud set outside the space, adjust the position of at least one boundary line in the plane structure diagram to obtain the adjusted plane structure diagram.

[0101] In this embodiment, since the plane structure diagram generated directly based on the original point cloud data may be inaccurate, for example, the point cloud through the window is identified as a point cloud within the space, and the boundary of the target space determined at this time is inaccurate. In order to solve this technical problem, this embodiment determines which point clouds are within the target space and which point clouds are outside the target space by determining a point cloud set within the space and a point cloud set outside the space, and adjusts the plane structure diagram based on this, so that the boundary line can be adjusted to the correct position more accurately.

[0102] The method for processing a plane structure diagram provided by the above-mentioned embodiment of the present disclosure includes: determining a plane structure diagram including a plurality of boundary lines based on the original point cloud data corresponding to the target space; obtaining a set of point clouds within the space and a set of point clouds outside the space according to the relevant information of the point clouds in the original point cloud data; the set of point clouds within the space includes at least one point cloud within the space, and the set of point clouds outside the space includes at least one point cloud outside the space; based on the set of point clouds within the space and the set of point clouds outside the space, adjusting the position of at least one boundary line in the plane structure diagram to obtain the adjusted plane structure diagram. The embodiment of the present disclosure realizes intelligent distinction of point clouds by determining a set of point clouds within the space and a set of point clouds outside the space, and adjusts the position of at least one boundary line in the plane structure diagram by using the set of point clouds within the space and the set of point clouds outside the space, so that the boundary lines in the plane structure diagram are adjusted to more accurate positions, thereby avoiding errors caused by data confusion (for example, identifying all original point cloud data as point clouds within the target space).

[0103] like Figure 2 As shown in the above Figure 1 Based on the illustrated embodiment, step 102 may include the following steps:

[0104] Step 1021 , performing semantic recognition on the original point cloud data, and obtaining a boundary point cloud set whose semantic recognition result is a boundary line.

[0105] Optionally, semantic recognition can be performed on the point clouds in the original point cloud data by any semantic recognition method in the prior art. For example, semantic recognition can be performed on each point cloud in the original point cloud data by using a semantic recognition network in a deep neural network to determine the semantic category corresponding to each point cloud. Boundary point clouds whose semantic recognition results are boundary lines are aggregated together to obtain a boundary point cloud set. In addition, the semantic recognition results can also include other categories. For example, when the target space is a room, the semantic recognition results can include: wall (boundary line), window, door, floor, etc.

[0106] Step 1022, based on the boundary point cloud set, determine the positions of multiple boundary lines to obtain a plane structure diagram.

[0107] The target space provided in this embodiment is a space with boundaries. Therefore, after determining the boundary lines, the outer frame of the plane structure diagram can be obtained by connecting the boundary lines to obtain the overall layout of the target space; the area enclosed by the outer frame can be cut off to obtain the plane structure diagram corresponding to the target space.

[0108] Based on the above embodiments of the present application, the following may also be included:

[0109] Determine point cloud intensity information and display difference information corresponding to at least one point cloud in the original point cloud data.

[0110] In this embodiment, the voltage intensity information obtained by the laser radar during the scanning process will be different due to the different materials being scanned. In addition, there is a significant difference in display between the point cloud passing through the translucent material and the point cloud hitting the solid material, that is, display difference information of the point cloud is obtained.

[0111] A position in the plane structure diagram connected to the outside of the space is determined according to point cloud strength information and display difference information of at least one point cloud.

[0112] This embodiment can identify the position in the target space that is connected to the outside of the space based on the difference in point cloud intensity information and the display difference information, such as identifying the position of a window in a room, and then mapping it to the corresponding position of a boundary line (e.g., a wall); achieving detailed representation of the target space; in addition, this embodiment can also determine the acquisition trajectory of the point cloud based on multiple points corresponding to the original point cloud data. Usually, the initial point of the acquisition trajectory and the position in the opposite direction of the next point can be determined as the entrance position of the target space. For example, when the target space is a room, it is specified in the acquisition to start the acquisition with the back facing the entrance door. After determining the initial point according to the acquisition trajectory, the position of the entrance door can be determined, thereby forming a plan structure diagram of the entire room.

[0113] In some optional embodiments, before step 1021, the following steps may also be included:

[0114] The point cloud density corresponding to at least one point cloud in the original point cloud data is determined, and the original point cloud data is denoised according to the point cloud density to obtain denoised point cloud data.

[0115] In this embodiment, there may be some discrete points in the original point cloud data. These point clouds are noise points and will affect the accuracy of the semantic recognition results. Therefore, this embodiment compares the point cloud density corresponding to each point cloud with a preset threshold, and determines the point cloud with a point cloud density less than the preset threshold as a noise point, removes these noise points, and realizes denoising of the original point cloud data.

[0116] Step 1021 may include:

[0117] Semantic recognition is performed on the denoised point cloud data to obtain a boundary point cloud set whose semantic recognition result is a boundary line.

[0118] This embodiment performs semantic recognition on the denoised point cloud data after denoising. Since the point cloud included in the denoised point cloud data does not contain noise points, interference information in the point cloud is reduced, thereby improving the accuracy of semantic recognition.

[0119] like Figure 3 As shown in the above Figure 1 Based on the illustrated embodiment, step 104 may include the following steps:

[0120] Step 1041 , determining relevant information of at least one point cloud in the original point cloud data.

[0121] In this embodiment, the original point cloud data includes the three-dimensional coordinates of each point cloud, the corresponding acquisition points and the point cloud intensity information when it is collected. When determining the relevant information of the point cloud, it can be obtained directly or after processing based on the point cloud information corresponding to the original point cloud data. For example, the point cloud distribution characteristics are determined based on the three-dimensional coordinates of the point cloud (the number and distance of point clouds in each point cloud period can be known in combination with the three-dimensional coordinates of all point clouds included in the original point cloud data, and the point cloud distribution characteristics can be determined); for example, the point cloud reflection intensity is directly used as the point cloud related information based on the point cloud intensity information; for example, the direct spatial relationship between each point cloud and the adjacent points can be determined based on the three-dimensional coordinates of the point cloud, and the point cloud trajectory information can be determined.

[0122] Step 1042, determining the category of the point cloud according to the relevant information of the point cloud.

[0123] Among them, the categories of point clouds include point clouds within space and point clouds outside space.

[0124] Optionally, the relevant information of the point cloud includes but is not limited to at least one of the following: point cloud reflection intensity, point cloud distribution characteristics, and point cloud trajectory information; the corresponding step 1042 may include but is not limited to at least one of the following:

[0125] According to the point cloud reflection intensity of the point cloud, it is determined whether the point cloud has passed through an object of a light-transmitting material during collection. In response to the point cloud having passed through an object of a light-transmitting material, the category of the point cloud is determined to be an out-of-space point cloud; in response to the point cloud not having passed through an object of a light-transmitting material, the category of the point cloud is determined to be an in-space point cloud.

[0126] This embodiment utilizes the reflection characteristics of laser radar scanning to realize the classification of point clouds. Since the reflection intensity of point clouds can be directly obtained when point clouds are collected, the category of point clouds can be determined quickly and accurately. Optionally, the reflection intensity of point clouds can be classified using a first preset neural network. By inputting the reflection intensity of point clouds into the first preset neural network, the classification result corresponding to the point clouds can be directly output.

[0127] According to the point cloud distribution characteristics of the point cloud, the density value of the point cloud is determined. In response to the density value being less than a preset value, the category of the point cloud is determined to be an out-of-space point cloud; in response to the density value being greater than or equal to the preset value, the category of the point cloud is determined to be an in-space point cloud.

[0128] In this embodiment, the point cloud distribution characteristics can be determined based on the three-dimensional coordinate information of the point cloud and the adjacent point clouds within a certain range around it. The density value of the point cloud can be determined based on the number of point clouds included in a certain area (for example, a cube of a preset volume). When the density value is less than the preset value, it means that the point cloud distribution in the area is relatively sparse, and the target space is usually a physical space. When objects such as boundary (wall) lines are collected, the density is relatively stable. Therefore, the point cloud can be classified based on density. Optionally, the preset value can be set based on the specific application scenario, which can be an empirical value or modified according to user needs. Optionally, the point cloud density value can be classified using a second preset neural network, and the classification result corresponding to the point cloud can be directly output by inputting the point cloud density value into the second preset neural network.

[0129] According to the point cloud trajectory information of the point cloud, the spatial relationship between the point cloud and the adjacent point clouds is determined. In response to the distance between the point cloud and the adjacent point clouds being greater than the preset distance, the category of the point cloud is determined to be an out-of-space point cloud; in response to the distance between the point cloud and the adjacent point clouds being less than or equal to the preset distance, the category of the point cloud is determined to be an in-space point cloud.

[0130] This embodiment can obtain the trajectory information between point clouds through the three-dimensional coordinate information corresponding to each point cloud in the original point cloud data, and then determine the spatial relationship between each point cloud and the adjacent point cloud. Usually, the distance between two adjacent point clouds in the point cloud trajectory in the target space is small. Therefore, this embodiment determines the point cloud category based on the distance between adjacent point clouds in the point cloud trajectory information. Optionally, the point cloud trajectory information can be classified using a third preset neural network, and the classification result corresponding to the point cloud can be directly output by inputting the point cloud trajectory information into the third preset neural network.

[0131] When combining the above two or three classification methods, the two classification results or the three classification results can be weightedly added to determine the classification result. Optionally, the weight value corresponding to each classification result can be set according to the specific application scenario; for example, the first preset neural network determines point cloud A: 0.9 is a point cloud outside the space, 0.1 is a point cloud inside the space, and the corresponding weight value is 0.4; the second preset neural network determines point cloud A: 0.8 is a point cloud outside the space, 0.2 is a point cloud inside the space, and the corresponding weight value is 0.3 ; The third preset neural network determines point cloud A: 0.85 is a point cloud outside the space, 0.15 is a point cloud inside the space, and the corresponding weight value is 0.3; the weighted summation of this example can obtain the probability that point cloud A is a point cloud outside the space: 0.9*0.4+0.8*0.3+0.85*0.3=0.855; the probability that point cloud A is a point cloud inside the space is: 0.1*0.4+0.2*0.3+0.15*0.3=0.145; that is, in this example, point cloud A is determined to be a point cloud outside the space.

[0132] Step 1043 , based on the category of at least one point cloud in the original point cloud data, obtain a point cloud set within the space and a point cloud set outside the space.

[0133] By clustering point clouds of the same category, a set of point clouds within the space and a set of point clouds outside the space can be obtained. Optionally, when displaying the plane structure diagram, point clouds of different categories are displayed in different display modes (for example, different colors or different styles), so that the user can identify the point cloud category at a glance; in addition, the adjustment of the plane structure diagram provided in this embodiment can also be adjusted according to user operations. By providing users with highlighted point clouds of different categories, the user is assisted in correcting the plane structure diagram, which improves work efficiency, reduces the time for users to manually search and compare, and improves verification accuracy, ensuring that users can accurately locate point cloud data and reducing the occurrence of errors.

[0134] Figure 4 FIG. 1 is a flow chart of a method for processing a planar structure diagram provided by another exemplary embodiment of the present disclosure. Figure 4 As shown, the method provided in this embodiment includes:

[0135] Step 402: Determine a plane structure diagram including a plurality of boundary lines based on the original point cloud data corresponding to the target space.

[0136] The specific implementation and technical effect of this step can be referred to step 102 in the above embodiment, which will not be repeated here.

[0137] Step 404: obtaining a point cloud set in space and a point cloud set outside space according to the relevant information of the point cloud in the original point cloud data, wherein the point cloud set in space includes at least one point cloud in space, and the point cloud set outside space includes at least one point cloud outside space.

[0138] The specific implementation and technical effect of this step can be referred to step 104 in the above embodiment, which will not be repeated here.

[0139] Step 406: Based on the point cloud set inside the space and the point cloud set outside the space, adjust the position of at least one boundary line in the plane structure diagram to obtain the adjusted plane structure diagram.

[0140] The specific implementation and technical effect of this step can be referred to step 106 in the above embodiment, which will not be repeated here.

[0141] Step 408 : determining a colored point cloud image corresponding to the target space based on the original point cloud data and at least one image corresponding to the target space.

[0142] In this embodiment, after collecting the original point cloud data, at least one image (for example, video frame data) is also collected of the target space, the original point cloud data is colored with at least one image, each image is matched to the corresponding position of the original point cloud data, and the color information in the collected target space is restored to obtain a colored point cloud map; in this embodiment, the position matching between the image and the point cloud data can be achieved based on the matching method provided by the prior art, for example, based on the posture matching of the acquisition device for collecting point cloud data and collecting images, etc.

[0143] Step 410: determine a transformation matrix between the colored point cloud image and the plane structure image based on the original point cloud data.

[0144] In this embodiment, the coordinate system of the colored point cloud image is consistent with the coordinate system of the original point cloud data, and the plane structure image is determined based on the perspective conversion of the original point cloud data to the top view. Therefore, based on the transformation matrix corresponding to the coordinate system conversion between the original point cloud data and the plane structure image, the transformation matrix between the colored point cloud image and the plane structure image can be determined, and the colored point cloud image and the plane structure image are associated based on the transformation matrix, so that any operation in the plane structure image can be reflected in the corresponding position in the colored point cloud image.

[0145] Step 412: Based on the transformation matrix, the colored point cloud image and the plane structure image are displayed on the same display interface.

[0146] In this embodiment, the plane structure diagram and the original point cloud data are kept in the same coordinate system, and the transformation matrix for display is stored in the description file, so that the editor can click any position in the plane structure diagram and it can correspond to the colored point cloud diagram. This improves the matching verification efficiency between the point cloud data and the plane structure diagram, and reduces the time for users to switch between different views. In some optional examples, taking a house as an example, Figure 5As shown, a shaded point cloud image and a plane structure image are displayed in one display interface (this example only shows the structural correspondence), wherein the left side is the plane structure image and the right side is the shaded point cloud image. In this display interface, any editing operation can be performed on the plane structure image, and the area corresponding to the operation will be highlighted in the shaded point cloud image.

[0147] In some optional embodiments, the method provided by the embodiment of the present disclosure may further include:

[0148] In response to receiving at least one operation instruction through the plane structure diagram, a corresponding operation process is performed on the plane structure diagram according to the operation instruction to obtain a processing structure diagram.

[0149] Optionally, the operating instructions may include but are not limited to height adjustment instructions and / or position adjustment instructions.

[0150] Correspondingly, the plane structure diagram is processed according to the operation instruction to obtain a processing structure diagram, including:

[0151] According to the position and adjustment value corresponding to the height adjustment instruction, the height of the corresponding position in the plane structure diagram is adjusted so that the adjusted height value corresponds to the adjustment value, and a processing structure diagram is obtained. In this embodiment, the position corresponding to the operation instruction can be determined by selecting or boxing an area with a mouse, and the input of the adjustment value can be realized by inputting a specific numerical value. After receiving the instruction to select or box a position, the background of the display interface of this embodiment locks the corresponding position, and after receiving the input adjustment value, the height value of the corresponding position is adjusted to obtain a processing structure diagram.

[0152] and / or,

[0153] According to the adjustment object, adjustment direction and adjustment distance corresponding to the position adjustment instruction, the adjustment object is adjusted in the adjustment direction and the adjustment distance is obtained to obtain a processing structure diagram.

[0154] Similar to the height adjustment, in this embodiment, the object corresponding to the operation instruction (for example, a boundary line or a window, etc.) can be determined by selecting or framing an area with the mouse, and a specific distance value and direction can be input to adjust the distance and direction (according to the preset setting, moving to the left is positive, moving to the right is negative; or the preset setting is moving upward is positive, moving downward is negative, etc.), or the adjustment object can be adjusted in the adjustment direction and distance by dragging the mouse. After receiving the instruction to select or frame the adjustment object, the background of the display interface of this embodiment locks the corresponding adjustment object, and after receiving the input adjustment direction and adjustment distance, the adjustment object is adjusted in the adjustment direction and distance to obtain a processing structure diagram.

[0155] The processing structure diagram is displayed in the display interface, and the first operation area corresponding to the operation instruction is highlighted in the colored point cloud diagram.

[0156] The disclosed embodiment provides a method that supports the linkage between point cloud data and plane structure diagrams, aiming to improve the efficiency and accuracy of plane structure diagram editing. Through the method provided in this embodiment, the user clicks to select any position in the plane structure diagram, which can automatically identify and highlight the area in the corresponding colored point cloud diagram, assisting the user to quickly verify. Through this linkage mechanism, the user can intuitively compare the matching of the plane structure diagram and the colored point cloud diagram, and promptly discover and correct inconsistencies. This linkage mechanism not only greatly improves the matching verification efficiency between the plane structure diagram and the colored point cloud diagram, but also greatly reduces the time it takes for the user to switch between different views.

[0157] In addition, the method provided by the embodiment of the present disclosure also supports the differentiated display of point cloud data inside and outside the space. The method provided by any of the above embodiments can classify the point cloud data inside the space and the point cloud outside the space, and display the point cloud inside the space and the point cloud outside the space in different colors or styles. This differentiated display method enables users to understand and operate point cloud data more intuitively, so as to more accurately edit and correct the plane structure diagram, for example, adjust the position of the boundary line corresponding to the plane structure diagram with reference to the boundary between the point cloud inside the space and the point cloud outside the space.

[0158] Optionally, highlighting a first operation area corresponding to the operation instruction in the colored point cloud image includes:

[0159] A second operation area of ​​the operation instruction in the plane structure diagram is determined based on the difference between the processing structure diagram and the plane structure diagram.

[0160] In this embodiment, the second operation area is the area where the plane structure diagram is processed according to the operation instruction; optionally, the second operation area can be determined based on the difference between the processing structure diagram and the plane structure diagram, or, optionally, the second operation area in the plane structure diagram is determined based on the position corresponding to the operation instruction or the operation object.

[0161] Based on the transformation matrix and the second operation region, a first operation region in the shaded point cloud image is determined.

[0162] In this embodiment, the transformation matrix determines the conversion relationship between the plane structure diagram and the shaded point cloud diagram. Therefore, after determining the second operation area in the plane structure diagram, the corresponding first operation area to be operated can be determined in the shaded point cloud diagram in combination with the transformation matrix, and the first operation area can be highlighted in the display interface (for example, enlarged or displayed in different colors, etc.). The disclosed embodiment overlays and displays a point cloud top view (viewing the original point cloud data from a top-down angle, displaying point cloud classification in the point cloud top view, and displaying point clouds inside and outside the space in different colors or formats) and a plane structure diagram. The user adjusts the position of the boundary line in the plane structure diagram according to the point cloud classification result provided by the point cloud top view. According to the differentiated indoor and outdoor display effects, the user can enter numbers for fine-tuning, or drag lines to keep the plane structure diagram consistent with the point cloud top view. Then, the user can quickly confirm whether doors and windows are missing in the data through the colored point cloud and CAD files, and supports the linked display of the colored point cloud diagram and the plane structure diagram. After clicking on the line of the plane structure diagram, it will automatically jump to the corresponding position of the colored point cloud diagram. In addition, the user can also convert the perspective of the colored point cloud diagram, compare the facade point cloud and door and window schematic boxes of the colored point cloud diagram, and adjust the size of the selection box in the plane structure diagram to keep it consistent with the colored point cloud diagram, so as to complete the adjustment of the door and window heights.

[0163] In terms of drawing efficiency of the same set of target space point clouds, the plane structure drawing processing method provided by this implementation significantly shortens the editing time compared to AutoCAD. It not only simplifies the operation process, but also improves the accuracy and consistency of data processing, and ultimately achieves more efficient point cloud wireframe data drawing.

[0164] Any of the plane structure diagram processing methods provided in the embodiments of the present disclosure can be executed by any appropriate device with data processing capabilities, including but not limited to: terminal devices and servers, etc. Alternatively, any of the plane structure diagram processing methods provided in the embodiments of the present disclosure can be executed by a processor, such as the processor executing any of the plane structure diagram processing methods mentioned in the embodiments of the present disclosure by calling corresponding instructions stored in a memory. This will not be described in detail below.

[0165] Exemplary Devices

[0166] Figure 6 FIG. 1 is a schematic diagram of a planar structure graph processing device provided by an exemplary embodiment of the present disclosure. Figure 6 As shown, the device provided in this embodiment includes:

[0167] The structure diagram determining module 61 is used to determine a plane structure diagram including a plurality of boundary lines based on the original point cloud data corresponding to the target space.

[0168] The point cloud classification module 62 is used to obtain a point cloud set inside the space and a point cloud set outside the space according to relevant information of the point cloud in the original point cloud data.

[0169] The in-space point cloud set includes at least one in-space point cloud, and the out-space point cloud set includes at least one out-space point cloud.

[0170] The structure diagram adjustment module 63 is used to adjust the position of at least one boundary line in the plane structure diagram based on the point cloud set in the space and the point cloud set outside the space to obtain an adjusted plane structure diagram.

[0171] The plane structure diagram processing device provided by the above-mentioned embodiment of the present disclosure includes: determining a plane structure diagram including a plurality of boundary lines based on the original point cloud data corresponding to the target space; obtaining a set of point clouds within the space and a set of point clouds outside the space according to the relevant information of the point clouds in the original point cloud data; the set of point clouds within the space includes at least one point cloud within the space, and the set of point clouds outside the space includes at least one point cloud outside the space; based on the set of point clouds within the space and the set of point clouds outside the space, adjusting the position of at least one boundary line in the plane structure diagram to obtain the adjusted plane structure diagram. The embodiment of the present disclosure realizes intelligent distinction of point clouds by determining a set of point clouds within the space and a set of point clouds outside the space, and adjusting the position of at least one boundary line in the plane structure diagram by using the set of point clouds within the space and the set of point clouds outside the space, so that the boundary lines in the plane structure diagram are adjusted to more accurate positions, thereby avoiding errors caused by data confusion (for example, identifying all original point cloud data as point clouds within the target space).

[0172] In some optional embodiments, the structure diagram determining module 61 includes:

[0173] The semantic recognition unit is used to perform semantic recognition on the original point cloud data, and obtain a boundary point cloud set whose semantic recognition result is a boundary line.

[0174] The boundary determination unit is used to determine the positions of multiple boundary lines based on the boundary point cloud set to obtain a plane structure diagram.

[0175] Optionally, the structure diagram determining module 61 may further include:

[0176] The connected position determination unit is used to determine the point cloud strength information and display difference information corresponding to at least one point cloud in the original point cloud data; based on the point cloud strength information and display difference information of at least one point cloud, determine the position in the plane structure diagram that is connected to the outside of the space.

[0177] Optionally, the structure diagram determining module 61 may further include:

[0178] The denoising unit is used to determine the point cloud density corresponding to at least one point cloud in the original point cloud data, and perform denoising processing on the original point cloud data according to the point cloud density to obtain denoised point cloud data.

[0179] The semantic recognition unit is used to perform semantic recognition on the denoised point cloud data, and obtain a boundary point cloud set whose semantic recognition result is a boundary line.

[0180] In some optional embodiments, the point cloud classification module 62 includes:

[0181] A point cloud category determination unit is used to determine relevant information of at least one point cloud in the original point cloud data; and determine the category of the point cloud according to the relevant information of the point cloud; the category of the point cloud includes point clouds within the space and point clouds outside the space;

[0182] The point cloud collection unit is used to obtain an in-space point cloud collection and an out-space point cloud collection based on a category of at least one point cloud in the original point cloud data.

[0183] Optionally, the relevant information of the point cloud includes at least one of the following: point cloud reflection intensity, point cloud distribution characteristics, and point cloud trajectory information;

[0184] The point cloud category determination unit is specifically used to determine whether the point cloud has passed through an object of light-transmitting material during collection based on the point cloud reflection intensity of the point cloud, and determine the category of the point cloud as a point cloud outside the space in response to the point cloud passing through an object of light-transmitting material; and / or, determine the density value of the point cloud based on the point cloud distribution characteristics of the point cloud, and determine the category of the point cloud as a point cloud outside the space in response to the density value being less than a preset value; and / or, determine the spatial relationship between the point cloud and adjacent point clouds based on the point cloud trajectory information of the point cloud, and determine the category of the point cloud as a point cloud outside the space in response to the distance between the point cloud and adjacent point clouds being greater than a preset distance.

[0185] In some optional embodiments, the apparatus provided by the present disclosure may further include:

[0186] A point cloud coloring module, used to determine a colored point cloud image corresponding to the target space based on the original point cloud data and at least one image corresponding to the target space;

[0187] The image display module is used to determine the transformation matrix between the colored point cloud image and the plane structure image based on the original point cloud data; based on the transformation matrix, the colored point cloud image and the plane structure image are displayed on the same display interface.

[0188] Optionally, the device provided by the present disclosure may further include:

[0189] A structure diagram operation module, configured to, in response to receiving at least one operation instruction through the plane structure diagram, perform corresponding operation processing on the plane structure diagram according to the operation instruction to obtain a processing structure diagram;

[0190] A synchronous display module is used to display the processing structure diagram in the display interface and highlight the first operation area corresponding to the operation instruction in the colored point cloud diagram.

[0191] Optionally, the operation instruction includes a height adjustment instruction and / or a position adjustment instruction;

[0192] The structure diagram operation module is specifically used to adjust the height of the corresponding position in the plane structure diagram according to the position and adjustment value corresponding to the height adjustment instruction, so that the adjusted height value corresponds to the adjustment value, and obtain the processing structure diagram; and / or, according to the adjustment object, adjustment direction and adjustment distance corresponding to the position adjustment instruction, adjust the adjustment distance of the adjustment object toward the adjustment direction to obtain the processing structure diagram.

[0193] Optionally, a synchronous display module is specifically used to determine a second operation area of ​​the operation instruction in the plane structure diagram based on the difference between the processing structure diagram and the plane structure diagram; and to determine a first operation area in the shaded point cloud diagram based on the transformation matrix and the second operation area.

[0194] Exemplary Electronic Devices

[0195] Below, reference Figure 7 The electronic device according to the embodiment of the present disclosure is described. The electronic device may be any one or both of the first device and the second device, or a stand-alone device independent of them, and the stand-alone device may communicate with the first device and the second device to receive the collected input signals from them.

[0196] Figure 7 A block diagram of an electronic device according to an embodiment of the present disclosure is illustrated.

[0197] like Figure 7 As shown, the electronic device includes one or more processors and memory.

[0198] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0199] The memory may store one or more computer program products, and the memory may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program products may be stored on the computer-readable storage medium, and the processor may run the computer program product to implement the planar structure diagram processing method of each embodiment of the present disclosure described above and / or other desired functions.

[0200] In one example, the electronic device may further include: an input device and an output device, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0201] In addition, the input device may also include, for example, a keyboard, a mouse, and the like.

[0202] The output device can output various information to the outside, including determined distance information, direction information, etc. The output device can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0203] Of course, to simplify, Figure 7 Only some of the components related to the present disclosure in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device may further include any other appropriate components.

[0204] In addition to the above-mentioned methods and devices, an embodiment of the present disclosure may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps in the planar structure diagram processing method according to various embodiments of the present disclosure described in the above part of this specification.

[0205] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the disclosed embodiments, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0206] In addition, an embodiment of the present disclosure may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the planar structure diagram processing method according to various embodiments of the present disclosure described in the above part of this specification.

[0207] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0208] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0209] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0210] The block diagrams of the devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including," "comprising," "having," and the like are open words, referring to "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or," and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0211] The method and apparatus of the present disclosure may be implemented in many ways. For example, the method and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0212] It should also be noted that in the apparatus, device and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0213] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0214] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for processing a planar structure diagram, characterized in that: include: Based on the original point cloud data corresponding to the target space, a plane structure diagram including a plurality of boundary lines is determined; According to the relevant information of the point cloud in the original point cloud data, a point cloud set in space and a point cloud set outside space are obtained; the point cloud set in space includes at least one point cloud in space, and the point cloud set outside space includes at least one point cloud outside space; Based on the point cloud set within the space and the point cloud set outside the space, the position of at least one boundary line in the plane structure diagram is adjusted to obtain the adjusted plane structure diagram.

2. The method according to claim 1, characterized in that The step of obtaining a point cloud set in space and a point cloud set outside space according to the relevant information of the point cloud in the original point cloud data includes: Determining relevant information of at least one point cloud in the original point cloud data; Determine the category of the point cloud according to the relevant information of the point cloud; the category of the point cloud includes the point cloud within the space and the point cloud outside the space; Based on the category of at least one point cloud in the original point cloud data, the in-space point cloud set and the out-space point cloud set are obtained.

3. The method according to claim 1 or 2, characterized in that: The determining of a plane structure diagram including a plurality of boundary lines based on the original point cloud data corresponding to the target space includes: Performing semantic recognition on the original point cloud data to obtain a boundary point cloud set whose semantic recognition result is a boundary line; Based on the boundary point cloud set, the positions of the plurality of boundary lines are determined to obtain the plane structure diagram.

4. The method according to claim 3, characterized in that Also includes: Determining point cloud intensity information and display difference information corresponding to at least one point cloud in the original point cloud data; According to the point cloud intensity information and the display difference information of the at least one point cloud, a position in the plane structure diagram connected to the outside of the space is determined.

5. The method according to claim 3 or 4, characterized in that: Before performing semantic recognition on the original point cloud data to obtain a boundary point cloud set whose semantic recognition result is a boundary line, the method further includes: Determine a point cloud density corresponding to at least one point cloud in the original point cloud data, and perform denoising processing on the original point cloud data according to the point cloud density to obtain denoised point cloud data; The performing semantic recognition on the original point cloud data to obtain a boundary point cloud set whose semantic recognition result is a boundary line includes: Semantic recognition is performed on the denoised point cloud data to obtain a boundary point cloud set whose semantic recognition result is a boundary line.

6. The method according to any one of claims 1 to 5, characterized in that: Also includes: Determine a colored point cloud image corresponding to the target space based on the original point cloud data and at least one image corresponding to the target space; Determine a transformation matrix between the colored point cloud image and the plane structure image based on the original point cloud data; Based on the transformation matrix, the colored point cloud image and the plane structure image are displayed on the same display interface.

7. The method according to claim 6, characterized in that Also includes: In response to receiving at least one operation instruction through the plane structure diagram, performing corresponding operation processing on the plane structure diagram according to the operation instruction to obtain a processing structure diagram; The processing structure diagram is displayed in the display interface, and the first operation area corresponding to the operation instruction is highlighted in the colored point cloud diagram.

8. The method according to claim 7, characterized in that The operation instructions include height adjustment instructions and / or position adjustment instructions; The performing corresponding operation processing on the plane structure diagram according to the operation instruction to obtain the processing structure diagram includes: According to the position and adjustment value corresponding to the height adjustment instruction, the height of the corresponding position in the plane structure diagram is adjusted so that the adjusted height value corresponds to the adjustment value, thereby obtaining the processing structure diagram; and / or, According to the adjustment object, adjustment direction and adjustment distance corresponding to the position adjustment instruction, the adjustment object is adjusted to the adjustment direction by the adjustment distance to obtain the processing structure diagram.

9. The method according to claim 7 or 8, characterized in that: The step of highlighting the first operation area corresponding to the operation instruction in the colored point cloud image includes: Determine a second operation area of ​​the operation instruction in the plane structure diagram based on a difference between the processing structure diagram and the plane structure diagram; Based on the transformation matrix and the second operation area, a first operation area in the shaded point cloud image is determined.

10. A planar structure diagram processing device, characterized in that: include: A structure diagram determination module, used to determine a plane structure diagram including a plurality of boundary lines based on original point cloud data corresponding to the target space; A point cloud classification module, used to obtain a point cloud set in space and a point cloud set outside space according to relevant information of the point cloud in the original point cloud data; the point cloud set in space includes at least one point cloud in space, and the point cloud set outside space includes at least one point cloud outside space; The structure diagram adjustment module is used to adjust the position of at least one boundary line in the plane structure diagram based on the point cloud set in the space and the point cloud set outside the space to obtain the adjusted plane structure diagram.

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