Methods and apparatus for processing planar structural diagrams

Abstract

This disclosure provides a method and apparatus for processing planar structure diagrams. The method includes: determining a planar structure diagram including multiple boundary lines based on original point cloud data corresponding to a target space; obtaining an intra-space point cloud set and an extra-space point cloud set based on relevant information of the point clouds in the original point cloud data; the intra-space point cloud set including at least one intra-space point cloud, and the extra-space point cloud set including at least one extra-space point cloud; and adjusting the position of at least one boundary line in the planar structure diagram based on the intra-space point cloud set and the extra-space point cloud set to obtain the adjusted planar structure diagram.

Description

Technical Field

[0001] This disclosure relates to Internet technology, and in particular to a method and apparatus for processing planar structural diagrams. Background Technology

[0002] Computer-aided design (CAD) utilizes computer technology and specific software to create, modify, and optimize design drawings. Compared to traditional hand drafting, CAD offers greater efficiency and accuracy. Through CAD, designers and engineers can generate design drawings more quickly and perform precise measurements and analysis. CAD has applications in many fields, but its use is particularly significant in architectural design, enabling architects to transform design concepts into concrete floor plans and three-dimensional models. Summary of the Invention

[0003] The embodiments of this disclosure provide a method and apparatus for processing planar structural diagrams.

[0004] According to one aspect of the present disclosure, a method for processing planar structural diagrams is provided, including:

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

[0006] Based on the relevant information of the point cloud in the original point cloud data, a spatial point cloud set and an external point cloud set are obtained; the spatial point cloud set includes at least one spatial point cloud, and the external point cloud set includes at least one external point cloud.

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

[0008] Optionally, obtaining the spatial point cloud set and the spatial point cloud set based on relevant information of the point cloud in the original point cloud data includes:

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

[0010] Based on the relevant information of the point cloud, the category of the point cloud is determined; the category of the point cloud includes point clouds within the space and point clouds outside the space.

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

[0012] Optionally, determining a planar structure map including multiple boundary lines based on the original point cloud data corresponding to the target space includes:

[0013] Semantic recognition is performed on the original point cloud data to obtain a set of boundary point clouds whose semantic recognition results are boundary lines;

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

[0015] Optionally, it also includes:

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

[0017] Based on the point cloud intensity information and display difference information of the at least one point cloud, the location in the planar structure diagram that is connected to the outside space is determined.

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

[0019] 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 based on the point cloud density to obtain denoised point cloud data.

[0020] The step of performing semantic recognition on the original point cloud data to obtain a set of boundary point clouds whose semantic recognition results are boundary lines includes:

[0021] Semantic recognition is performed on the denoised point cloud data to obtain a set of boundary point clouds whose semantic recognition results are boundary lines.

[0022] Optionally, it also includes:

[0023] Based on the original point cloud data and at least one image corresponding to the target space, determine the colored point cloud map corresponding to the target space;

[0024] Based on the original point cloud data, determine the transformation matrix between the colored point cloud map and the planar structure map;

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

[0026] Optionally, it also includes:

[0027] In response to receiving at least one operation instruction through the planar structure diagram, the planar structure diagram is subjected to corresponding operation processing according to the operation instruction to obtain a processed 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 instructions include height adjustment instructions and / or position adjustment instructions;

[0030] The step of performing corresponding operation processing on the planar structure diagram according to the operation instruction to obtain a processed structure diagram includes:

[0031] Based on the position and adjustment value corresponding to the height adjustment command, the height of the corresponding position in the planar structure diagram is adjusted so that the adjusted height value corresponds to the adjustment value, thereby obtaining the processed structure diagram; and / or,

[0032] Based on the adjustment object, adjustment direction, and adjustment distance corresponding to the position adjustment command, the adjustment distance is adjusted to the adjustment object in the adjustment direction to obtain the processing structure diagram.

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

[0034] The second operation area of ​​the operation instruction in the planar structure diagram is determined based on the difference between the processing structure diagram and the planar structure diagram;

[0035] Based on the transformation matrix and the second operation region, the first operation region in the colored point cloud map is determined.

[0036] According to another aspect of the embodiments of this disclosure, a planar structural diagram processing apparatus is provided, comprising:

[0037] The structure diagram determination module is used to determine a planar structure diagram including multiple boundary lines based on the original point cloud data corresponding to the target space.

[0038] The point cloud classification module is used to obtain a spatial point cloud set and an external point cloud set based on the relevant information of the point cloud in the original point cloud data; the spatial point cloud set includes at least one spatial point cloud, and the external point cloud set includes at least one external point cloud.

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

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

[0041] 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 to determine the category of the point cloud based on 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] A point cloud set unit is used to obtain the spatial point cloud set and the spatial point cloud set based on the category of at least one point cloud in the original point cloud data.

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

[0044] A semantic recognition unit is used to perform semantic recognition on the original point cloud data to obtain a set of boundary point clouds whose semantic recognition result is a boundary line.

[0045] A boundary determination unit is used to determine the positions of the multiple boundary lines based on the boundary point cloud set, thereby obtaining the planar structure diagram.

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

[0047] The connectivity location determination unit is used to determine the point cloud intensity information and display difference information corresponding to at least one point cloud in the original point cloud data; and to determine the location of the connection with the outside space in the planar structure diagram based on the point cloud intensity information and display difference information of the at least one point cloud.

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

[0049] A denoising unit is used to determine the 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 set of boundary point clouds with the semantic recognition result being the boundary line.

[0051] Optionally, the device further includes:

[0052] The point cloud coloring module is used to determine the colored point cloud map corresponding to the target space based on the original point cloud data and at least one image corresponding to the target space.

[0053] The graph display module is used to determine the transformation matrix between the colored point cloud map and the planar structure map based on the original point cloud data; and to display the colored point cloud map and the planar structure map on the same display interface based on the transformation matrix.

[0054] Optionally, the device further includes:

[0055] The structural diagram operation module is used to respond to receiving at least one operation instruction through the planar structural diagram, and to perform corresponding operation processing on the planar structural diagram according to the operation instruction to obtain a processed structural diagram.

[0056] The 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 instructions include height adjustment instructions and / or position adjustment instructions;

[0058] The structure diagram operation module is specifically used to adjust the height of the corresponding position in the planar 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, thereby obtaining the processed 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 in the adjustment direction, thereby obtaining the processed structure diagram.

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

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

[0061] Memory, used to store computer program products;

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

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

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

[0065] The planar structure diagram processing method and apparatus provided in the above embodiments of this disclosure include: determining a planar structure diagram including multiple boundary lines based on original point cloud data corresponding to a target space; obtaining an intra-space point cloud set and an extra-space point cloud set according to relevant information of the point clouds in the original point cloud data; the intra-space point cloud set including at least one intra-space point cloud, and the extra-space point cloud set including at least one extra-space point cloud; and adjusting the position of at least one boundary line in the planar structure diagram based on the intra-space point cloud set and the extra-space point cloud set to obtain the adjusted planar structure diagram. This disclosure achieves intelligent differentiation of point clouds by determining the intra-space point cloud set and the extra-space point cloud set, and adjusts the position of at least one boundary line in the planar structure diagram using the intra-space point cloud set and the extra-space point cloud set, thereby adjusting the boundary line in the planar structure diagram to a more accurate position and avoiding errors caused by data confusion.

[0066] The technical solutions of this disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

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

[0068] This disclosure will become clearer with reference to the accompanying drawings and the following detailed description, wherein:

[0069] Figure 1 This is a schematic flowchart of a planar structure diagram processing method provided in an exemplary embodiment of this disclosure;

[0070] Figure 2 This is a public announcement Figure 1 A flowchart illustrating step 102 in the illustrated embodiment;

[0071] Figure 3 This is a public announcement Figure 1 A flowchart illustrating step 104 in the illustrated embodiment;

[0072] Figure 4 This is a flowchart illustrating a planar structure diagram processing method provided in another exemplary embodiment of this disclosure;

[0073] Figure 5 This is a schematic diagram showing the interface in a planar structure diagram processing method provided in an exemplary embodiment of this disclosure;

[0074] Figure 6 This is a schematic diagram of the planar structural diagram processing apparatus provided in 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 shown. Detailed Implementation

[0076] Hereinafter, exemplary embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present disclosure, and not all embodiments of the present disclosure, and it should be understood that the present disclosure is not limited to the exemplary embodiments described herein.

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

[0078] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of this disclosure are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0079] It should also be understood that in the embodiments disclosed herein, "a plurality of" may refer to two or more, and "at least one" may refer to one, two or more.

[0080] It should also be understood that any component, data or structure mentioned in the embodiments of this disclosure can generally be understood as one or more unless expressly defined or given to the contrary in the context.

[0081] Furthermore, the term "and / or" in this disclosure is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this disclosure generally indicates that the preceding and following related objects have an "or" relationship. The data referred to in this disclosure can include unstructured data such as text, images, and videos, as well as structured data.

[0082] It should also be understood that the description of the various embodiments in this disclosure emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0083] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0084] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use.

[0085] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

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

[0087] The embodiments disclosed herein can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose 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, and servers 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 PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.

[0088] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, 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 distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0089] Application Overview

[0090] In developing this disclosure, the inventors discovered that measuring the room is the most crucial step in the renovation business, but it is labor-intensive, human error is difficult to control effectively, and it is hard to establish a standardized operating procedure. Existing technology uses laser acquisition equipment to scan a room, collecting point cloud data, and then uses AI algorithms to automatically generate a structural CAD drawing of the house. However, the results output by the AI ​​algorithm may contain errors.

[0091] Exemplary methods

[0092] Figure 1 This is a schematic flowchart of a planar structure diagram processing method provided in an exemplary embodiment of this disclosure. This embodiment can be applied to electronic devices, such as... Figure 1 As shown, it includes the following steps:

[0093] Step 102: Based on the original point cloud data corresponding to the target space, determine the planar structure diagram including multiple boundary lines.

[0094] Optionally, the planar structure diagram can be a CAD structure diagram. In this embodiment, a planar structure diagram can be obtained using a method that can be implemented in the prior art to generate a planar structure diagram from point cloud data. For example, the original point cloud data is transformed into a top-down view of the target space through coordinate system transformation, and then coordinate processing is performed (for example, all z-axis coordinates are set to 0) to obtain a planar structure diagram in the top-down view. The planar structure diagram displays the boundary of the target space (e.g., walls, etc.) and at least one structure included in the space (e.g., doors, windows, etc.).

[0095] In this embodiment, the target space can be any space including boundaries, such as a room or a vehicle. For example, when the target space is a room, the original point cloud data is obtained by acquiring a 3D point cloud using a laser acquisition device at at least one point within the room.

[0096] Step 104: Based on the relevant information of the point cloud in the original point cloud data, obtain the spatial point cloud set and the spatial point cloud set.

[0097] The spatial point cloud set includes at least one spatial point cloud, and the spatial point cloud set includes at least one spatial point cloud. In this embodiment, the original point cloud data is collected within the target space. The target space may contain at least one transparent material with light transmission (e.g., a window). Point clouds outside the target space can also be collected through these light transmission points. Therefore, this embodiment classifies the original point cloud data into spatial point cloud sets and spatial point cloud sets.

[0098] Optionally, each point cloud in the original point cloud data includes three-dimensional spatial coordinates, corresponding point location 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. The point cloud reflection intensity is the reflection intensity value returned by the LiDAR during the scanning process. This value is related to factors such as the surface material, color, and roughness of the object. This embodiment mainly reflects the difference in reflection intensity due to different materials, because point clouds acquired outside the target space are usually obtained through transparent materials (e.g., glass). The transparent material differs from the boundary material (wall), resulting in significant differences in reflection intensity. Point cloud distribution characteristics correspond to point cloud density. Point clouds acquired within the target space typically have a higher density and are uniformly distributed, while point clouds acquired through transparent materials within the target space typically have a lower density and are discretely distributed. Point cloud trajectory information is determined based on the spatial relationship between adjacent points in the point cloud. The point cloud trajectory information can be determined by the number and distance of adjacent points in each point cloud cycle. The trajectory information of point clouds within the target space is relatively similar, while the trajectory information of point clouds outside the target space is significantly different from that of point clouds within the target space.

[0099] In some embodiments, point clouds are classified using relevant information to classify the point clouds in the original point cloud data into intra-space point clouds or extra-space point clouds.

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

[0101] In this embodiment, the planar structure diagram generated directly from the original point cloud data may be inaccurate. For example, point clouds seen through a window may be identified as point clouds within the space, resulting in inaccurate determination of the target space boundary. To address this issue, this embodiment determines which point clouds are within the target space and which are outside by identifying both the set of point clouds within and outside the space. Based on this, the planar structure diagram is adjusted, allowing for more accurate alignment of the boundary lines to the correct positions.

[0102] The planar structure diagram processing method provided in the above embodiments of this disclosure includes: determining a planar structure diagram including multiple boundary lines based on the original point cloud data corresponding to the target space; obtaining an intra-space point cloud set and an extra-space point cloud set according to the relevant information of the point cloud in the original point cloud data; the intra-space point cloud set includes at least one intra-space point cloud, and the extra-space point cloud set includes at least one extra-space point cloud; adjusting the position of at least one boundary line in the planar structure diagram based on the intra-space point cloud set and the extra-space point cloud set to obtain the adjusted planar structure diagram. This disclosure achieves intelligent differentiation of point clouds by determining the intra-space point cloud set and the extra-space point cloud set, and adjusts the position of at least one boundary line in the planar structure diagram using the intra-space point cloud set and the extra-space point cloud set, thereby adjusting the boundary line in the planar structure diagram to a more accurate position and avoiding errors caused by data confusion (e.g., identifying all original point cloud data as intra-space point clouds).

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

[0104] Step 1021: Perform semantic recognition on the original point cloud data to obtain a set of boundary point clouds with the semantic recognition result being the boundary line.

[0105] Optionally, semantic recognition can be performed on the point clouds in the original point cloud data using any existing semantic recognition method. For example, a semantic recognition network in a deep neural network can be used to perform semantic recognition on each point cloud in the original point cloud data to determine the semantic category corresponding to each point cloud. The 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: walls (boundary lines), windows, doors, floors, etc.

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

[0107] In this embodiment, the target space provided is a space with boundaries. Therefore, after determining the boundary lines, connecting the boundary lines will yield the outer frame of the planar structure diagram and the overall layout of the target space. The area enclosed by the outer frame can be cut into the planar structure diagram corresponding to the target space.

[0108] Based on the above embodiments of this application, it may further include:

[0109] Determine the 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 LiDAR during the scanning process will differ due to the different materials being scanned. In addition, there is a significant difference in the display between the point cloud that passes through the light-transmitting material and the point cloud that hits the solid material, that is, the display difference information of the point cloud is obtained.

[0111] Based on the point cloud intensity information and display difference information of at least one point cloud, determine the location in the planar structure diagram that is connected to the outside space.

[0112] Based on the differences in point cloud intensity information and display differences, this embodiment can identify the locations in the target space that connect to the outside space, such as the location of a window in a room, and then map it to the corresponding location on the boundary line (e.g., a wall); thus achieving a 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 position of the initial point of the acquisition trajectory in the opposite direction to the next point can be determined as the entrance position of the target space. For example, when the target space is a room, the acquisition process is specified to start with the back to the entrance door. After determining the initial point based on the acquisition trajectory, the position of the entrance door can be determined, thereby forming a plan view of the entire room.

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

[0114] 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 based on 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, which will affect the accuracy of semantic recognition results. Therefore, this embodiment compares the point cloud density corresponding to each point cloud with a preset threshold, and identifies point clouds with a point cloud density less than the preset threshold as noise points. These noise points are removed to achieve 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 set of boundary point clouds whose semantic recognition results are boundary lines.

[0118] This embodiment performs semantic recognition on the denoised point cloud data after denoising processing. Since the point cloud data includes no noise, the interference information in the point cloud is reduced, thus improving the accuracy of semantic recognition.

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

[0120] Step 1041: Determine the 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 point, and point cloud intensity information during acquisition. 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 can be determined based on the three-dimensional coordinates of the point cloud (by combining the three-dimensional coordinates of all point clouds included in the original point cloud data, the number and distance of point clouds in each point cloud period can be known, thereby determining the point cloud distribution characteristics); another example is that the point cloud intensity information can be directly used as the point cloud reflection intensity in the relevant point cloud information; yet another example is that the spatial relationship between each point cloud and its adjacent points can be determined based on the three-dimensional coordinates of the point cloud, thereby determining the point cloud trajectory information.

[0122] Step 1042: Determine the category of the point cloud based on the relevant information of the point cloud.

[0123] Point clouds are categorized into intra-space point clouds and extra-space point clouds.

[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] Based on the point cloud's reflection intensity, determine whether the point cloud passed through a translucent object during acquisition. If the point cloud passed through a translucent object, the point cloud is classified as an out-of-space point cloud; if the point cloud did not pass through a translucent object, the point cloud is classified as an in-space point cloud.

[0126] This embodiment utilizes the reflection characteristics of lidar scanning to classify point clouds. Since the reflection intensity of the point cloud can be directly obtained during point cloud acquisition, the category of the point cloud can be determined quickly and accurately. Optionally, a first preset neural network can be used to classify the reflection intensity of the point cloud. By inputting the reflection intensity of the point cloud into the first preset neural network, the classification result corresponding to the point cloud is directly output.

[0127] Based on the point cloud distribution characteristics, the density value of the point cloud is determined. If the density value is less than a preset value, the point cloud is classified as an out-of-space point cloud; if the density value is greater than or equal to the preset value, the point cloud is classified as 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 its neighboring point clouds within a certain range. The point cloud density value can be determined based on the number of point clouds included in a certain area (e.g., a cube with a preset volume). When the density value is less than the preset value, it indicates that the point cloud distribution in that area is relatively sparse. Since the target space is usually a physical space, the density of objects such as boundary (wall) lines is relatively stable when collecting point clouds. Therefore, the point cloud can be classified based on density. Optionally, the preset value can be set based on specific application scenarios, and can be an empirical value or modified according to user needs. Optionally, a second preset neural network can be used to classify the point cloud density value. By inputting the point cloud density value into the second preset neural network, the classification result corresponding to the point cloud is directly output.

[0129] Based on the point cloud trajectory information, the spatial relationship between the point cloud and adjacent point clouds is determined. If the distance between the point cloud and adjacent point clouds is greater than a preset distance, the point cloud is classified as an out-of-space point cloud; if the distance between the point cloud and adjacent point clouds is less than or equal to a preset distance, the point cloud is classified as an in-space point cloud.

[0130] This embodiment uses the three-dimensional coordinate information corresponding to each point cloud in the original point cloud data to obtain the trajectory information between point clouds, and then determines the spatial relationship between each point cloud and its neighboring point clouds. Typically, the distance between two adjacent point clouds in the point cloud trajectory within 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, a third preset neural network can be used to classify the point cloud trajectory information. By inputting the point cloud trajectory information into the third preset neural network, the classification result corresponding to the point cloud is directly output.

[0131] When combining two or three of the above classification methods, the two or three classification results can be weighted and summed to determine the final 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 as follows: 0.9 for point cloud outside space, 0.1 for point cloud inside space, with a corresponding weight value of 0.4; the second preset neural network determines point cloud A as follows: 0.8 for point cloud outside space, 0.2 for point cloud inside space, with a corresponding weight value of 0.3. The third preset neural network determines point cloud A as follows: 0.85 for point cloud outside space and 0.15 for point cloud inside space, with a corresponding weight value of 0.3. In this example, the weighted summation yields the following probabilities: the probability that point cloud A is an outside point cloud is: 0.9*0.4 + 0.8*0.3 + 0.85*0.3 = 0.855; the probability that point cloud A is an inside point cloud 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 an outside point cloud.

[0132] Step 1043: Based on the category of at least one point cloud in the original point cloud data, obtain the spatial point cloud set and the spatial point cloud set.

[0133] By clustering point clouds of the same category, both spatial and spatial point cloud sets can be obtained. Optionally, when displaying the planar structure diagram, different categories of point clouds can be displayed using different display methods (e.g., different colors or different styles), allowing users to easily identify the point cloud categories. In addition, the planar structure diagram provided in this embodiment can be adjusted according to user operations. By providing users with highlighted point clouds of different categories, it assists users in correcting the planar structure diagram, improving work efficiency, reducing the time spent on manual searching and comparison, and enhancing verification accuracy. This ensures that users can accurately locate point cloud data and reduce the generation of errors.

[0134] Figure 4 This is a flowchart illustrating a planar structural diagram processing method provided in another exemplary embodiment of this disclosure. For example... Figure 4 As shown, the method provided in this embodiment includes:

[0135] Step 402: Based on the original point cloud data corresponding to the target space, determine a planar structure diagram including multiple boundary lines.

[0136] The specific implementation and technical effects of this step can be found in step 102 of the above embodiments, and will not be repeated here.

[0137] Step 404: Based on the relevant information of the point clouds in the original point cloud data, obtain the spatial point cloud set and the spatial point cloud set. The spatial point cloud set includes at least one spatial point cloud, and the spatial point cloud set includes at least one spatial point cloud.

[0138] The specific implementation and technical effects of this step can be found in step 104 of the above embodiments, and will not be repeated here.

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

[0140] The specific implementation and technical effects of this step can be found in step 106 of the above embodiments, and will not be repeated here.

[0141] Step 408: Based on the original point cloud data and at least one image corresponding to the target space, determine the colored point cloud map corresponding to the target space.

[0142] In this embodiment, after acquiring the original point cloud data, at least one image (e.g., video frame data) is also acquired in the target space. The original point cloud data is then colored using at least one image. Each image is matched to the corresponding position in the original point cloud data to restore the color information in the acquired target space, thus obtaining 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 existing technology, such as the pose matching based on the acquisition device that acquires the point cloud data and the acquired image.

[0143] Step 410: Determine the transformation matrix between the colored point cloud map and the planar structure map based on the original point cloud data.

[0144] In this embodiment, the coordinate system of the colored point cloud map is consistent with the coordinate system of the original point cloud data. The planar structure map is determined based on the angle of the original point cloud data after the viewpoint transformation to the top view. Therefore, based on the transformation matrix corresponding to the coordinate system transformation between the original point cloud data and the planar structure map, the transformation matrix between the colored point cloud map and the planar structure map can be determined. Based on this transformation matrix, the colored point cloud map and the planar structure map are associated, so that any operation in the planar structure map can be reflected at the corresponding position in the colored point cloud map.

[0145] Step 412: Based on the transformation matrix, display the shading point cloud map and the planar structure map on the same display interface.

[0146] In this embodiment, the planar structure diagram and the original point cloud data are kept in the same coordinate system. Simultaneously, the transformation matrix for display is stored in the description file, allowing the editor to automatically map any click on a location in the planar structure diagram to the colored point cloud diagram. This improves the matching and verification efficiency between the point cloud data and the planar structure diagram, and reduces the time spent by the user switching between different views. In some optional examples, taking a house as an example, such as... Figure 5As shown, a display interface shows a colored point cloud map and a planar structure map (this example is only to show the structural correspondence). The left side is the planar structure map, and the right side is the colored point cloud map. In this display interface, the planar structure map can be edited arbitrarily, and the corresponding area will be highlighted in the colored point cloud map.

[0147] In some optional embodiments, the method provided in this disclosure may further include:

[0148] In response to receiving at least one operation instruction through the planar structure diagram, the planar structure diagram is processed accordingly based on the operation instruction to obtain a processed 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 planar structure diagram is processed according to the operation instructions to obtain a processed structure diagram, including:

[0151] Based on the position and adjustment value corresponding to the height adjustment command, the height of the corresponding position in the planar structure diagram is adjusted so that the adjusted height value corresponds to the adjustment value, thus obtaining the processed structure diagram. In this embodiment, the position corresponding to the operation command can be determined by selecting or boxing an area with the mouse, and the adjustment value can be input by entering a specific value. In this embodiment, the backend of the interface locks the corresponding position after receiving the command to select or box the position, and adjusts the height value of the corresponding position after receiving the input adjustment value to obtain the processed structure diagram.

[0152] And / or,

[0153] Based on the adjustment object, adjustment direction, and adjustment distance corresponding to the position adjustment command, the adjustment object is adjusted in the adjustment direction and the adjustment distance is adjusted to obtain the processing structure diagram.

[0154] Similar to height adjustment, in this embodiment, the object corresponding to the operation command can be determined by selecting or boxing an area with the mouse (e.g., a boundary line or a window). The specific distance value and direction can be input to adjust the distance and direction (which can be preset to be positive for moving left and negative for moving right; or preset to be positive for moving up and negative for moving down, etc.). Alternatively, the object can be adjusted by dragging the mouse in the adjustment direction. In this embodiment, after receiving the instruction to select or box the object, the backend of the interface locks the corresponding object. After receiving the input adjustment direction and distance, the object is adjusted in the adjustment direction to obtain the processing structure diagram.

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

[0156] This disclosure provides a method for linking point cloud data and planar structure diagrams, aiming to improve the efficiency and accuracy of planar structure diagram editing. Using the method provided in this embodiment, when a user clicks and selects any location in the planar structure diagram, the corresponding area in the colored point cloud diagram is automatically identified and highlighted, assisting the user in quick verification. Through this linkage mechanism, users can intuitively compare the matching between the planar structure diagram and the colored point cloud diagram, promptly identifying and correcting inconsistencies. This linkage mechanism not only significantly improves the matching verification efficiency between the planar structure diagram and the colored point cloud diagram but also greatly reduces the time users spend switching between different views.

[0157] Furthermore, the method provided in this disclosure also supports differentiated display of point cloud data inside and outside space. The method provided in any of the above embodiments can classify point clouds inside and outside space in the point cloud data, and display them with different colors or styles. This differentiated display method allows users to understand and manipulate point cloud data more intuitively, thereby enabling more accurate editing and correction of planar structure diagrams. For example, the position of the boundary line corresponding to the planar structure diagram can be adjusted by referring to the boundary between the point clouds inside and outside space.

[0158] Optionally, the first operation area corresponding to the operation command is highlighted in the colored point cloud map, including:

[0159] The second operation area of ​​the operation instruction in the planar structure diagram is determined based on the differences between the processing structure diagram and the planar structure diagram.

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

[0161] The first operational region in the colored point cloud map is determined based on the transformation matrix and the second operational region.

[0162] In this embodiment, the transformation matrix determines the transformation relationship between the planar structure diagram and the colored point cloud diagram. Therefore, after determining the second operation area in the planar structure diagram, the corresponding first operation area to be operated can be determined in the colored point cloud diagram by combining the transformation matrix, and the first operation area can be highlighted in the display interface (e.g., enlarged or displayed with different colors). This embodiment of the disclosure overlays 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 through different colors or formats) and a planar structure diagram. Users can adjust the position of the boundary lines in the planar structure diagram based on the point cloud classification results provided by the point cloud top view. According to the differentiated indoor and outdoor display effects, users can input numbers for fine-tuning or drag lines to make the planar structure diagram consistent with the point cloud top view. Then, users can quickly confirm whether the data is missing doors and windows through the colored point cloud and CAD files. It supports the linked display of the colored point cloud diagram and the planar structure diagram. After clicking on the lines in the planar structure diagram, it will automatically jump to the corresponding position of the colored point cloud diagram. In addition, users can also change the perspective of the colored point cloud diagram, compare the elevation point cloud and the door and window schematic frames in the colored point cloud diagram, and adjust the size of the selection box in the planar structure diagram to make it consistent with the colored point cloud diagram, thereby completing the adjustment of the door and window height.

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

[0164] Any of the planar structure diagram processing methods provided in this disclosure can be executed by any suitable device with data processing capabilities, including but not limited to: terminal devices and servers. Alternatively, any of the planar structure diagram processing methods provided in this disclosure can be executed by a processor, such as by a processor executing any of the planar structure diagram processing methods mentioned in this disclosure by calling corresponding instructions stored in memory. Further details will not be elaborated below.

[0165] Exemplary device

[0166] Figure 6 This is a schematic diagram of the planar structural diagram processing apparatus provided in an exemplary embodiment of this disclosure. For example... Figure 6 As shown, the apparatus provided in this embodiment includes:

[0167] The structure diagram determination module 61 is used to determine a planar structure diagram including multiple 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 the spatial point cloud set and the spatial point cloud set based on the relevant information of the point cloud in the original point cloud data.

[0169] The set of point clouds within space includes at least one point cloud within space, and the set of point clouds outside space includes at least one point cloud outside space.

[0170] The structure diagram adjustment module 63 is used to adjust the position of at least one boundary line in the planar structure diagram based on the spatial point cloud set and the spatial point cloud set, so as to obtain the adjusted planar structure diagram.

[0171] The planar structure diagram processing apparatus provided in the above embodiments of this disclosure includes: determining a planar structure diagram including multiple boundary lines based on original point cloud data corresponding to a target space; obtaining an intra-space point cloud set and an extra-space point cloud set according to relevant information of the point clouds in the original point cloud data; the intra-space point cloud set includes at least one intra-space point cloud, and the extra-space point cloud set includes at least one extra-space point cloud; adjusting the position of at least one boundary line in the planar structure diagram based on the intra-space point cloud set and the extra-space point cloud set to obtain the adjusted planar structure diagram. This disclosure achieves intelligent differentiation of point clouds by determining the intra-space point cloud set and the extra-space point cloud set, and adjusts the position of at least one boundary line in the planar structure diagram using the intra-space point cloud set and the extra-space point cloud set, thereby adjusting the boundary line in the planar structure diagram to a more accurate position and avoiding errors caused by data confusion (e.g., identifying all original point cloud data as intra-space point clouds).

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

[0173] The semantic recognition unit is used to perform semantic recognition on the original point cloud data to obtain a set of boundary point clouds with the semantic recognition result being the boundary line.

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

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

[0176] The connectivity location determination unit is used to determine the point cloud intensity information and display difference information corresponding to at least one point cloud in the original point cloud data; and to determine the location of the connection with the outside space in the planar structure diagram based on the point cloud intensity information and display difference information of at least one point cloud.

[0177] Optionally, the structure diagram determination 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 to denoise 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 to obtain a set of boundary point clouds with the semantic recognition result as the boundary line.

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

[0181] The point cloud category determination unit is used to determine the relevant information of at least one point cloud in the original point cloud data; and to determine the category of the point cloud based on the relevant information of the point cloud; the category of point cloud includes in-space point cloud and out-of-space point cloud;

[0182] A point cloud set unit is used to obtain an in-space point cloud set and an out-of-space point cloud set based on at least one point cloud category 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 passed through a translucent object during acquisition based on the point cloud reflection intensity; if the point cloud passed through a translucent object, the point cloud category is determined to be an out-of-space point cloud; and / or, to determine the point cloud density value based on the point cloud distribution characteristics; if the density value is less than a preset value, the point cloud category is determined to be an out-of-space point cloud; and / or, to determine the spatial relationship between the point cloud and adjacent point clouds based on the point cloud trajectory information; if the distance between the point cloud and adjacent point clouds is greater than a preset distance, the point cloud category is determined to be an out-of-space point cloud.

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

[0186] The point cloud coloring module is used to determine the colored point cloud map corresponding to the target space based on the original point cloud data and at least one image corresponding to the target space.

[0187] The graph display module is used to determine the transformation matrix between the colored point cloud map and the planar structure map based on the original point cloud data; and to display the colored point cloud map and the planar structure map on the same display interface based on the transformation matrix.

[0188] Optionally, the apparatus provided in this disclosure may further include:

[0189] The structural diagram operation module is used to respond to receiving at least one operation instruction through the planar structural diagram, and to perform corresponding operation processing on the planar structural diagram according to the operation instruction to obtain a processed structural diagram.

[0190] The 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 instructions include height adjustment instructions and / or position adjustment instructions;

[0192] The structure diagram operation module is specifically used to adjust the height of the corresponding position in the planar 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, thereby obtaining the processed 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 in the adjustment direction, thereby obtaining the processed structure diagram.

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

[0194] Exemplary electronic devices

[0195] Below, for reference Figure 7 This describes an electronic device according to embodiments of the present disclosure. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

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

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

[0198] A processor can be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and can control other components in an electronic device to perform desired functions.

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

[0200] In one example, the electronic device may also include input devices and output devices, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

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

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

[0203] Of course, for the sake of simplicity, Figure 7 Only some of the components of the electronic device relevant to this disclosure are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0204] In addition to the methods and apparatus described above, embodiments of this disclosure may also be computer program products, including computer program instructions that, when executed by a processor, cause the processor to perform the steps in the planar structural diagram processing methods according to various embodiments of this disclosure as described in the foregoing portion of this specification.

[0205] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this disclosure. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on a user's computing device, partially on a user's computing device, as a standalone software package, partially on a user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0206] Furthermore, embodiments of this disclosure may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the planar structural diagram processing methods according to various embodiments of this disclosure as described in the foregoing portion of this specification.

[0207] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0208] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0209] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0210] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0211] The methods and apparatus of this disclosure may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of this disclosure are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, this disclosure may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this disclosure. Thus, this disclosure also covers recording media storing programs for performing the methods according to this disclosure.

[0212] It should also be noted that in the apparatus, devices, and methods of this disclosure, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions to this disclosure.

[0213] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this 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 this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0214] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for processing planar structural diagrams, characterized in that, include: Based on the original point cloud data corresponding to the target space, a planar structure diagram including multiple boundary lines is determined; Based on the relevant information of the point cloud in the original point cloud data, a spatial point cloud set and an external point cloud set are obtained; the spatial point cloud set includes at least one spatial point cloud, and the external point cloud set includes at least one external point cloud. Based on the point cloud set inside the space and the point cloud set outside the space, the position of at least one boundary line in the planar structure diagram is adjusted to obtain the adjusted planar structure diagram. Also includes: Based on the original point cloud data and at least one image corresponding to the target space, determine the colored point cloud map corresponding to the target space; Based on the original point cloud data, determine the transformation matrix between the colored point cloud map and the planar structure map; Based on the transformation matrix, the colored point cloud map and the planar structure map are displayed on the same display interface.

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

3. The method according to claim 1, characterized in that, The determination of a planar structure diagram including multiple boundary lines based on the original point cloud data corresponding to the target space includes: Semantic recognition is performed on the original point cloud data to obtain a set of boundary point clouds whose semantic recognition results are boundary lines; Based on the boundary point cloud set, the positions of the multiple boundary lines are determined to obtain the planar structure diagram.

4. The method according to claim 3, characterized in that, Also includes: Determine the point cloud intensity information and display difference information corresponding to at least one point cloud in the original point cloud data; Based on the point cloud intensity information and display difference information of the at least one point cloud, the location in the planar structure diagram that is connected to the outside space is determined.

5. The method according to claim 3, characterized in that, Before performing semantic recognition on the original point cloud data to obtain a set of boundary point clouds whose semantic recognition result is a boundary line, the process further includes: 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 based on the point cloud density to obtain denoised point cloud data. The step of performing semantic recognition on the original point cloud data to obtain a set of boundary point clouds whose semantic recognition results are boundary lines includes: Semantic recognition is performed on the denoised point cloud data to obtain a set of boundary point clouds whose semantic recognition results are boundary lines.

6. The method according to any one of claims 1-5, characterized in that, Also includes: In response to receiving at least one operation instruction through the planar structure diagram, the planar structure diagram is subjected to corresponding operation processing according to the operation instruction to obtain a processed 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.

7. The method according to claim 6, characterized in that, The operation commands include height adjustment commands and / or position adjustment commands; The step of performing corresponding operation processing on the planar structure diagram according to the operation instruction to obtain a processed structure diagram includes: Based on the position and adjustment value corresponding to the height adjustment command, the height of the corresponding position in the planar structure diagram is adjusted so that the adjusted height value corresponds to the adjustment value, thereby obtaining the processed structure diagram; and / or, Based on the adjustment object, adjustment direction, and adjustment distance corresponding to the position adjustment command, the adjustment distance is adjusted to the adjustment object in the adjustment direction to obtain the processing structure diagram.

8. The method according to claim 6, characterized in that, The step of highlighting the first operation area corresponding to the operation instruction in the colored point cloud map includes: The second operation area of ​​the operation instruction in the planar structure diagram is determined based on the difference between the processing structure diagram and the planar structure diagram; Based on the transformation matrix and the second operation region, the first operation region in the colored point cloud map is determined.

9. A planar structural diagram processing device, characterized in that, include: The structure diagram determination module is used to determine a planar structure diagram including multiple boundary lines based on the original point cloud data corresponding to the target space. The point cloud classification module is used to obtain a spatial point cloud set and an external point cloud set based on the relevant information of the point cloud in the original point cloud data; the spatial point cloud set includes at least one spatial point cloud, and the external point cloud set includes at least one external point cloud. The structure diagram adjustment module is used to adjust the position of at least one boundary line in the planar structure diagram based on the point cloud set inside the space and the point cloud set outside the space, so as to obtain the adjusted planar structure diagram. The device further includes: The point cloud coloring module is used to determine the colored point cloud map corresponding to the target space based on the original point cloud data and at least one image corresponding to the target space. The graph display module is used to determine the transformation matrix between the colored point cloud map and the planar structure map based on the original point cloud data; and to display the colored point cloud map and the planar structure map on the same display interface based on the transformation matrix.

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