Three-dimensional modeling method for point cloud data and point cloud data processing system

Abstract

The invention discloses a point cloud data three-dimensional modeling method and a point cloud data processing system, and the method comprises the steps: data acquisition: receiving point cloud data scanned by laser radar equipment, and achieving the acquisition of model basic data; s2, filtering processing: carrying out data filtering processing, identifying and removing invalid interference points in the point cloud data in the S1 by adopting a density clustering and statistical filtering algorithm, and improving the modeling precision; and S2, generating a model: generating a surface and wireframe structure of the three-dimensional model by using the point cloud data filtered in the S2 based on a box expansion algorithm, and outputting the surface and wireframe structure in a custom JSON format. The method has the beneficial effects that irrelevant point cloud data are automatically filtered out through a filtering algorithm, the model precision is improved, errors generated by manual intervention are reduced, the modeling precision and reliability are ensured, efficiency and accuracy are considered through a box expansion algorithm, the method adapts to shaft parallel and inclined wall surfaces, and a lightweight JSON format is adopted to facilitate data exchange and secondary development.

Description

Technical Field

[0001] The present invention relates to the technical field of building BIM modeling, and in particular to a three-dimensional modeling method of point cloud data and a point cloud data processing system. Background Art

[0002] In the fields of building renovation and BIM modeling, point cloud data processing and 3D modeling tools based on LiDAR scanning have become an indispensable part. However, existing 3D modeling tools have significant limitations when processing point cloud data of rough housing, mainly reflected in the following aspects:

[0003] 1. Inefficient processing: Traditional modeling tools typically rely on manual processing of point cloud data, point-by-point analysis and modeling. This process is extremely time-consuming, especially when dealing with large-scale data sets, where efficiency issues become increasingly prominent.

[0004] 2. High sensitivity to interference data: In a rough-finished house environment, the walls may contain non-planar structures such as small cement lumps, grooves, and plumbing joints. These interference points are often mistakenly identified as valid structures by traditional algorithms, resulting in reduced model accuracy.

[0005] 3. Complex output formats are not conducive to application: Existing 3D modeling tools mostly use complex output formats such as FBX and OBJ. Although these formats can accurately describe 3D models, their large amount of data and difficulty in parsing make them inconvenient for direct application in engineering analysis and design systems, especially when data exchange with professional software such as BIM is required.

[0006] 4. Lack of dynamic adjustment capabilities: Once a model is generated, most modeling tools are difficult to modify in real time. Users cannot adjust model parameters according to actual needs or site conditions, which limits the flexibility and practicality of the tool. Summary of the Invention

[0007] The purpose of this invention is to address the fact that existing 3D modeling tools often rely on manual processing of point cloud data, which is inefficient and sensitive to interference points in rough housing (such as uneven wall surfaces and pipeline joints), resulting in insufficient model accuracy. Traditional algorithms have difficulty distinguishing between valid structure and noise, and the output formats are complex (such as FBX and OBJ), making them unsuitable for direct use in engineering analysis. In addition, existing tools lack dynamic adjustment capabilities, preventing users from modifying model parameters in real time according to actual needs. The present invention provides a 3D modeling method and point cloud data processing system based on point cloud data.

[0008] The purpose of the present invention is achieved through the following technical solutions: A three-dimensional modeling method of point cloud data, comprising:

[0009] S1, data acquisition: receiving the point cloud data scanned by the lidar device and the panoramic image taken by the image acquisition device, processing the point cloud data into octree point cloud tiles to achieve model basic data acquisition;

[0010] S2, hydropower site marking: The panoramic image captured by the image acquisition device automatically identifies and matches the hydropower sites in the hydropower site database, and marks the hydropower sites in the point cloud data;

[0011] S3, filtering processing: The point cloud data marked with hydropower points in S2 is filtered, and density clustering and statistical filtering algorithms are used to identify and remove invalid interference points in the point cloud data in S1 to improve modeling accuracy;

[0012] S4, generate model: first place the hydropower points marked and matched in S2 point cloud data into the 3D model. S3 generates the surface and wireframe structure of the 3D model based on the box expansion algorithm based on the point cloud data after filtering, and outputs it in a custom JSON format.

[0013] A further technical solution is that the invalid interference points in S2 include invalid concave and convex points on the wall, invalid points on pipeline joints, and invalid points on human shadows.

[0014] A further technical solution is that the expansion box algorithm in S3 places a space box in the point cloud data space. The expansion box algorithm automatically expands from small to large when placing the expansion box. The space box includes door and window expansion boxes and wall expansion boxes, and supports specified expansion in horizontal and vertical directions. The door and window expansion box first expands to generate the door and window model, and then the wall expansion box is expanded last to generate the three-dimensional model of the apartment room. During the expansion process, the wall expansion box automatically passes through the marked water and electricity points. The wall expansion box performs shear processing when encountering the marked door and window model, and prioritizes plane fitting based on the indoor wall surface. The expansion box algorithm sets the pass rate threshold according to the point cloud density and automatically calculates the wall expansion boundary.

[0015] The spatial box automatically expands and adapts its shape according to the point cloud data. It determines straight walls and curved walls based on the flatness and density of the point cloud data and calculates the curvature of the curved walls.

[0016] A further technical solution is that the custom JSON format in S3 includes the following data structure:

[0017] Wall: centerline starting point, end point and thickness;

[0018] Doors and windows: center point coordinates, width, height;

[0019] Cylinder: center point coordinates and length, width or radius;

[0020] Beam body: centerline starting point, end point, width and height;

[0021] Water and electricity point information: category attributes (water, electricity, heating) and the center point coordinates, width, and height of the corresponding category.

[0022] A further technical solution is that the point cloud data scanned by the lidar device supports .las and .e57 formats.

[0023] A point cloud data processing system, comprising:

[0024] a data entry module for executing item S1 of claim 1, inputting point cloud data scanned by the lidar device and panoramic images captured by the image acquisition device into the system;

[0025] a pre-processing module, configured to perform octree point cloud tile processing on input point cloud data as described in item S1 of claim 1;

[0026] a point cloud filtering module, configured to execute item S3 of claim 1, identifying and removing invalid interference points in the point cloud data;

[0027] a three-dimensional modeling module, configured to execute item S4 of claim 1, and generate a surface and wireframe structure of a three-dimensional model based on a box expansion algorithm;

[0028] A data output module, configured to execute the custom JSON format output of claim 4, including model information;

[0029] A visualization display module for displaying the three-dimensional model generated by item S4 of claim 1, supporting switching between coloring, wireframe, and point cloud modes;

[0030] The measurement module is used to mark the length and angle in the real environment on the point cloud or model, and is calculated based on the model data first, and supports automatic calibration in the horizontal and vertical directions;

[0031] The editing module allows users to dynamically adjust the model plane position and supports numerical input and mouse dragging operations.

[0032] A further technical solution is to also include a hydropower point library module. The data entry module is connected to the hydropower point library module, and is responsible for entering the panoramic image matching the point cloud data into the system, and automatically identifying and matching the hydropower points in the hydropower point library based on the image attributes obtained from the panoramic image. The three-dimensional modeling module automatically marks and penetrates the hydropower points based on the hydropower points identified by the data entry module, and generates a three-dimensional model using a box expansion algorithm.

[0033] A further technical solution is that the measurement module has the ability to eliminate interference from small objects on the ground to improve the accuracy of model adjustment.

[0034] The present invention has the following advantages: the present invention automatically filters out irrelevant point cloud data through a filtering algorithm, improves model accuracy, reduces errors caused by manual intervention, ensures the accuracy and reliability of modeling, takes into account both efficiency and accuracy through a box expansion algorithm, adapts to axis-parallel and inclined walls, and adopts a lightweight JSON format to facilitate data exchange and secondary development, and supports multiple data format inputs and custom output formats, has strong compatibility, and is convenient for integration and data exchange with other software systems. The point cloud data processing system integrates a measurement module and an editing module to realize dynamic adjustment functions, improves user experience, and enhances the practicality of the tool. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of point cloud data processing according to the present invention;

[0036] Figure 2 Schematic diagram of the operation of the box expansion algorithm of the present invention;

[0037] Figure 3 This is a schematic diagram of the interface of the visual display module of the present invention;

[0038] Figure 4 This is a schematic diagram of the expansion state interface of the door and window expansion box in the present invention;

[0039] Figure 5 This is a schematic diagram of the expansion state interface of the wall expansion box in the present invention; DETAILED DESCRIPTION

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0041] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are also within the scope of protection of the present invention.

[0042] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other.

[0043] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0044] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, or are the orientations or positional relationships in which the inventive product is typically placed when in use, or are the orientations or positional relationships commonly understood by those skilled in the art. These terms are intended only to facilitate the description of the present invention and to simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0045] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0046] like Figures 1 to 3 As shown, a three-dimensional modeling method of point cloud data includes:

[0047] S1, data acquisition: receiving the point cloud data scanned by the lidar device and the panoramic image taken by the image acquisition device, processing the point cloud data into octree point cloud tiles to achieve model basic data acquisition;

[0048] By importing point cloud data in .las or .e57 format and applying statistical filtering algorithms, we can effectively remove outliers and other invalid interference data, providing a high-quality point cloud data foundation for subsequent model generation. Specifically, the statistical filtering algorithm analyzes the distribution of point cloud data, identifies and removes outliers that deviate from the overall distribution, thereby improving the purity of the point cloud data and reducing the impact of invalid data on subsequent processing.

[0049] S2, Water and Power Point Marking: The panoramic image captured by the image acquisition device automatically identifies and matches the water and power points in the water and power point database and marks the water and power points in the point cloud data. The water and power points include water pipe points, power line points, switch points, socket points, faucet points, and drain points.

[0050] S3, filtering processing: The point cloud data marked with water and electricity points in S2 is filtered, and density clustering and statistical filtering algorithms are used to identify and remove invalid interference points in the point cloud data in S1 to improve modeling accuracy, such as small cement lumps, grooves, human shadows, and plumbing joints on the wall, which significantly improves the accuracy and efficiency of modeling. The filtering algorithm accurately filters out unstructured points through statistical analysis and clustering recognition, while the box expansion algorithm automatically calculates and generates the expansion boundary of the wall based on the distribution characteristics of the point cloud data to form the basic framework of the model. In terms of effect, the technology in this embodiment realizes automated three-dimensional modeling through efficient data processing and accurate model generation, greatly saving modeling time and improving modeling quality;

[0051] S4, generate model: first place the hydropower points marked and matched in S2 point cloud data into the 3D model. S3 generates the surface and wireframe structure of the 3D model based on the box expansion algorithm based on the point cloud data after filtering, and outputs it in a custom JSON format.

[0052] In the 2D view, users can draw a rectangular or L-shaped expansion box as needed, using it as a reference surface. Next, they set the expansion direction (horizontal and vertical directions are supported) and the pass rate threshold (e.g., 80%). The algorithm automatically fits the wall boundaries based on these parameters, generating the surface and wireframe structure of the 3D model. The following details several key technical aspects:

[0053] (1) Plane parameter definition

[0054] Define the RectanglePlane structure to describe the plane parameters, including the plane origin P0, the unit normal vector n, the two orthogonal unit vectors u_axis and v_axis within the plane, and the value ranges u_min, u_max, v_min, and v_max in the u and v directions. These parameters together define a plane region for subsequent point cloud projection and screening.

[0055] (2) Point cloud projection and distance calculation

[0056] The computeProjections function is used to project point cloud data onto a defined plane and calculate distances. The specific process is as follows:

[0057] Multi-threaded processing: Point cloud data is distributed to multiple threads for parallel processing, with each thread processing a portion of the point cloud data to improve computational efficiency. Memory is pre-allocated within each thread to store valid point coordinates and corresponding signed distances generated during processing.

[0058] SIMD acceleration: Utilizes the AVX instruction set to vectorize data, processing four points at a time. Vector operations are used to quickly calculate the distance between a point and a plane, as well as its projection coordinates within the plane, further improving computing speed.

[0059] Conditional screening: Valid points are screened based on whether their projected coordinates on the plane fall within the range of values ​​in the u and v directions. For points that meet the conditions, their coordinates and the signed distance to the plane are recorded.

[0060] (3) Data sorting and merging

[0061] Intra-thread sorting: After each thread processes the assigned data, it sorts the valid points according to the signed distance to the plane, generates a sort index and rearranges the data for subsequent merge operations.

[0062] Multi-way merging: Use a priority queue to merge the results of multi-threaded processing, and extract valid points from each thread in ascending order of distance to ensure that the merged data is ordered, which facilitates subsequent expansion calculations.

[0063] (4) Expansion algorithm execution

[0064] Execute the inflation algorithm in the InflateItemWidget::onCtrlClicked function. The specific steps are as follows:

[0065] Initialization and parameter setting: Get the point data and expansion box parameters set by the user, including the expansion box's reference point, direction vector, length, etc. Initialize the plane parameters based on the expansion box's geometric information and determine the direction and range of expansion.

[0066] Point cloud data preparation: Convert the point cloud data into the format required by the algorithm and extract the x, y, and z coordinates of the points for projection and distance calculation.

[0067] Iterative expansion processing:

[0068] For each expanded surface, the center, direction vector, and length of its bounding box are calculated, and the expanded boundary is automatically determined based on the point cloud distribution. The index position of the point with a distance of 0 from the plane is quickly found through bisection to determine the starting point of the expansion.

[0069] Based on the preset pass rate threshold and point cloud density, the expansion distance is calculated, the vertex coordinates of the expansion box are updated, and the new wall boundary is generated. During the update process, the model's rendering nodes are adjusted in real time to ensure that the user can intuitively see the expansion effect.

[0070] When dealing with vertical expansion, adjust the height of the plane origin and recalculate the projection and distance to ensure that the vertical expansion conforms to the actual wall structure.

[0071] Invalid interference points in S2 include invalid concave and convex points on the wall, invalid points on pipeline joints, and invalid points on human shadows.

[0072] The expansion box algorithm in S3 places a space box in the point cloud data space. The expansion box algorithm automatically expands from small to large when placing the expansion box. The space box includes door and window expansion boxes and wall expansion boxes, and supports specified expansion in horizontal and vertical directions. The door and window expansion box first expands to generate the door and window model. The door and window expansion box includes expansion boxes for door openings, window openings, columns, and beams. Then the wall expansion box is finally expanded to generate the three-dimensional model of the apartment room. During the expansion process, the wall expansion box automatically passes through the marked water and electricity points. The wall expansion box performs shear processing when encountering the marked door and window model, and prioritizes plane fitting based on the indoor wall surface. The expansion box algorithm sets the pass rate threshold according to the point cloud density and automatically calculates the wall expansion boundary.

[0073] The spatial box automatically expands and adapts its shape according to the point cloud data. It determines straight walls and curved walls based on the flatness and density of the point cloud data and calculates the curvature of the curved walls. The spatial box supports manual placement.

[0074] The custom JSON format in S3 includes the following data structures:

[0075] Wall: centerline starting point, end point and thickness;

[0076] Doors and windows: center point coordinates, width, height;

[0077] Cylinder: center point coordinates and length, width or radius;

[0078] Beam body: centerline starting point, end point, width and height;

[0079] Water and electricity point information: category attributes (water, electricity, heating) and the center point coordinates, width, and height of the corresponding category;

[0080] This lightweight JSON format facilitates data exchange and secondary development, and can be directly adapted to the BIM system interface to meet the needs of engineering analysis and subsequent applications.

[0081] LiDAR point cloud data scanned by LiDAR devices supports .las and .e57 formats, making this method compatible with mainstream LiDAR scan data formats on the market, enhancing its versatility and applicability. Technically, the tool uses a built-in format parser to quickly read and parse these specialized point cloud data formats, providing standardized data input for subsequent processing.

[0082] A point cloud data processing system, comprising:

[0083] a data entry module for executing item S1 of claim 1, inputting point cloud data scanned by the lidar device and panoramic images captured by the image acquisition device into the system;

[0084] a pre-processing module, configured to perform octree point cloud tile processing on input point cloud data as described in item S1 of claim 1;

[0085] The point cloud filtering module is used to execute item S3 in claim 1, identify and remove invalid interference points in the point cloud data, and adopt density clustering and statistical filtering algorithms, which enables the tool to effectively identify and remove abnormal points in the point cloud data and improve the purity of the model. Technically, the density clustering algorithm analyzes the local density of the point cloud data and regards high-density areas as valid structures, while low-density areas are regarded as noise or invalid points, thereby achieving accurate point cloud data cleaning. The statistical filtering algorithm identifies and removes outliers based on the global statistical characteristics of the point cloud data, such as average distance, standard deviation, etc. In principle, the two algorithms complement each other. Density clustering focuses on the identification of local structures, while statistical filtering focuses on the distribution of global data. Together, they can comprehensively remove various types of interference data. In terms of effect, the technology in this embodiment significantly improves the accuracy of the model, reduces errors in the subsequent modeling process, and improves the quality of the final model;

[0086] The point cloud filtering module removes invalid points such as bumps on the wall and pipe joints, which enables the tool to process point cloud data more finely and avoid these minor interference factors that affect the quality of modeling. Technically, by setting specific rules or thresholds, the tool can identify and remove these surface irregularities and unstructured elements to ensure that the modeling process is based only on valid point cloud data. In principle, through in-depth analysis of point cloud data, the tool can distinguish between the true outline of the wall and surface changes caused by construction defects or additional facilities, thereby generating a more accurate model. In terms of effect, the technology in this embodiment significantly improves the accuracy and aesthetics of the model by removing these interference points, reducing the workload of later manual corrections.

[0087] A three-dimensional modeling module is used to execute item S4 in claim 1, and generates the surface and wireframe structure of the three-dimensional model based on the box expansion algorithm. The box expansion algorithm supports specified expansion in the horizontal and vertical directions, which enables the tool to flexibly adapt to the structural characteristics of different wall surfaces, and to generate accurate models for walls in both horizontal and vertical directions. Technically, the box expansion algorithm fits the actual contour of the wall by automatically expanding the boundaries of the box in the specified direction. This process takes into account the distribution density of the point cloud data and follows the geometric characteristics of the wall. In principle, the algorithm first determines the initial position and direction of the expansion box, and then dynamically adjusts the size of the expansion box according to the distribution of the point cloud data until the predetermined pass rate threshold is reached. In terms of effect, the technology in this embodiment improves the flexibility and accuracy of model generation by supporting multi-directional expansion, and can adapt to various complex indoor environments.

[0088] The 3D modeling module prioritizes plane fitting based on interior walls. The generated model supports specified expansion in both horizontal and vertical directions, enabling the tool to accurately capture the wall's contours and generate a 3D model that closely matches the actual environment. Technically, the 3D modeling module automatically identifies the wall's reference plane by analyzing the distribution of point cloud data, and performs expansion based on this, ensuring that the model's surface and wireframe structure match the actual wall. In principle, the box expansion algorithm first determines the main direction of the wall, then, through an iterative expansion process, gradually approaches the actual boundary of the wall until the ideal fit is achieved.

[0089] The box expansion algorithm performs plane fitting based on the indoor wall surface, which enables the tool to accurately capture the outline of the wall surface and generate a three-dimensional model that highly matches the actual environment. Technically, the box expansion algorithm automatically identifies the reference plane of the wall surface by analyzing the distribution of point cloud data, and performs expansion processing based on this to ensure that the surface and wireframe structure of the model match the actual wall surface. In principle, the algorithm first determines the main direction of the wall surface, and then gradually approaches the actual boundary of the wall surface through an iterative expansion process until the ideal fitting effect is achieved. In terms of effect, the technology in this embodiment significantly improves the accuracy and authenticity of the model by fitting based on the wall surface, reducing the workload of subsequent adjustments.

[0090] A data output module, configured to execute the custom JSON format output of claim 4, including model information;

[0091] The information output by the data output module includes the center line of the wall, the coordinates and dimensions of doors and windows, and the geometric parameters of beams and columns. This allows the key information of the model to be stored and transmitted in a structured manner, facilitating subsequent analysis and application. Technically, by defining a detailed JSON data structure, the data output module can accurately record the geometric parameters of the model, including the center line of the wall, the specific position and dimensions of doors and windows, and the geometric information of beams and columns, providing a solid foundation for subsequent data processing and application. In principle, this structured data output method is not only easy for humans to read and understand, but also easy for computer programs to parse and use, and can effectively promote data sharing and reuse.

[0092] The surface and wireframe structure of the three-dimensional model are output based on the custom JSON format, including the geometric parameters of walls, doors and windows, beams and columns, which enables the model information to be stored and transmitted in a lightweight format, facilitating data exchange with other systems or software. Technically, through the custom JSON data structure, the tool can efficiently organize and store the geometric information of the model, including key parameters such as the position of walls, the size of doors and windows, and the structure of beams and columns. In principle, the JSON format is a lightweight data exchange format that is easy to read and parse, can effectively represent complex data structures, and is very suitable for the output of model information. In terms of effect, the technology in this embodiment simplifies the data transmission and parsing process by outputting data in JSON format, thereby improving compatibility and interoperability with third-party software such as BIM systems.

[0093] The output in JSON format includes the coordinates of the starting and ending points of the wall centerline, thickness, coordinates of the center points of doors and windows, and width and height parameters, as well as geometric information related to columns and beams. This enables the key information of the model to be stored in a structured manner, facilitating subsequent analysis and application. Technically, through a carefully designed JSON data structure, the tool can accurately record the geometric parameters of the model, including the center line of the wall, the specific position and size of doors and windows, and the geometric information of beams and columns, providing a solid foundation for subsequent data processing and application. In principle, this structured data output method is not only easy for humans to read and understand, but also easy for computer programs to parse and use, and can effectively promote data sharing and reuse. In terms of effect, the technology in this embodiment significantly improves the availability and value of model information by outputting detailed geometric parameters, reducing the workload of subsequent data processing.

[0094] A visualization display module for displaying the three-dimensional model generated by item S4 of claim 1, supporting switching between coloring, wireframe, and point cloud modes, providing a three-dimensional visualization interface, and supporting the user to perform the following operations: View switching: The user can freely switch between multiple modes such as coloring, wireframe, and point cloud, observe the model from different angles, and facilitate inspection of the details and overall structure of the model;

[0095] The measurement module is used to mark the length and angle in the real environment on the point cloud or model. It prioritizes calculations based on model data, supports automatic calibration in the horizontal and vertical directions, and eliminates interference factors such as small objects on the ground to ensure the accuracy of the measurement results;

[0096] The editing module is used to allow the user to dynamically adjust the plane position of the model, support numerical input and mouse dragging operations, dynamically adjust the model plane position, door and window positions and other parameters, modify the model in real time, and improve the flexibility and efficiency of modeling. The measurement module eliminates interference from small objects on the ground and supports automatic calibration in the horizontal and vertical directions. This enables the tool to provide high-precision measurement results and maintain accuracy even in complex indoor environments. Technically, by integrating an intelligent calibration algorithm, the tool can automatically identify and eliminate interference from small objects on the ground to ensure the purity of the measurement data. In principle, the measurement function is based on the geometric characteristics of the model data, and can automatically calibrate the horizontal and vertical angles to eliminate measurement errors caused by uneven ground or obstacles. In terms of effect, the technology in this embodiment significantly improves the accuracy and efficiency of measurement by providing an automatically calibrated measurement function, reducing the workload of manual correction.

[0097] The editing module supports numerical input and mouse dragging operations, which allows users to flexibly adjust the model according to actual needs, improving the flexibility and efficiency of modeling. Technically, by integrating the dynamic adjustment function, users can directly insert or modify the position of the model in the three-dimensional visualization interface without having to re-import point cloud data or make complex parameter adjustments, which greatly simplifies the modeling process. In principle, the dynamic adjustment function is based on a real-time model data update mechanism, which can instantly reflect the user's operations, provide intuitive visual feedback, and enhance the user experience. In terms of effect, the technology in this embodiment enables users to quickly respond to on-site changes by supporting dynamic adjustment, thereby improving the adaptability and efficiency of modeling.

[0098] It also includes a hydropower point library module and a door and window model library. The data entry module is responsible for entering panoramic images that match the point cloud data into the system, and automatically identifying and matching the hydropower points in the hydropower point library and the door and window models in the door and window model library based on the image attributes obtained from the panoramic image. The three-dimensional modeling module automatically marks and penetrates the hydropower points based on the hydropower points and door and window models identified by the data entry module, performs shearing on the door and window models, and generates a three-dimensional model using the box expansion algorithm.

[0099] The hydropower points in the hydropower point library and the door and window models in the door and window model library both support manual insertion and placement through the editing module.

[0100] The measurement module has the ability to eliminate interference from small objects on the ground to improve the accuracy of model adjustment.

[0101] The system is executed on a computer storage medium, and a computer program of the execution method is stored, which enables the tool to run on various computing platforms to meet the needs of different users. Technically, by encapsulating the modeling method as a computer program, the tool can fully utilize the performance of modern computer hardware to achieve efficient data processing and model generation. In principle, the program on the computer storage medium can be loaded and executed by the operating system, and by calling the corresponding algorithm library and function, the processing of point cloud data and the generation of three-dimensional models can be achieved. In terms of effect, the technology in this embodiment runs on a computer storage medium, making the tool have good portability and scalability, and can adapt to the ever-changing technical environment and user needs.

[0102] Through the above steps, the present invention realizes efficient and accurate point cloud data processing and three-dimensional model generation, reduces manual intervention, improves modeling efficiency and accuracy, and meets the needs of fields such as building decoration and BIM modeling.

[0103] The working process of the present invention is as follows: first, the user imports the point cloud data scanned by the lidar through the point cloud data processing tool, and then the system automatically performs data filtering to remove invalid interference points; then, the user draws an expansion box on the visualization interface through the visualization module, and the system automatically calculates the wall boundary according to the distribution of the point cloud data. The three-dimensional modeling module automatically marks and penetrates the water and electricity points according to the water and electricity points identified by the image input module, and uses the box expansion algorithm to generate the surface and wireframe structure of the three-dimensional model; finally, the user can dynamically adjust the model parameters through the editing module to complete the final three-dimensional model generation.

[0104] In practical application, the user first selects an appropriate LiDAR device to scan the interior of a rough-finished house, acquiring point cloud data. A panoramic camera then captures a panoramic image of the rough-finished house. The system then automatically processes the data and creates a model. After generating a preliminary model, the user can observe the model through a visual interface, verify dimensions using measurement tools, and finally make necessary adjustments using editing tools to ensure a close match between the model and the actual environment. This system and method are applicable to both single-story and multi-story rough-finished houses. The entire process embodies the tool's automated nature while retaining the user's ability to manually intervene, ensuring accurate and flexible model generation.

[0105] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A three-dimensional modeling method for point cloud data, characterized in that: include, S1, data acquisition: receiving point cloud data scanned by a laser radar device and a panoramic image captured by an image acquisition device, performing octree point cloud tile processing on the point cloud data to acquire basic model data; S2, hydropower site marking: The panoramic image captured by the image acquisition device automatically identifies and matches the hydropower sites in the hydropower site database, and marks the hydropower sites in the point cloud data; S3, filtering processing: performing data filtering on the point cloud data marked with hydropower points in S2, using density clustering and statistical filtering algorithms to identify and remove invalid interference points in the point cloud data in S1, thereby improving modeling accuracy; S4, generate model: first place the hydropower points marked and matched in S2 point cloud data into the 3D model. S3 generates the surface and wireframe structure of the 3D model based on the box expansion algorithm based on the point cloud data after filtering, and outputs it in a custom JSON format.

2. The three-dimensional modeling method of point cloud data according to claim 1, characterized in that: The invalid interference points in S2 include invalid concave-convex points on the wall, invalid points on pipeline joints, and invalid points on human shadows.

3. The three-dimensional modeling method of point cloud data according to claim 1, characterized in that: The expansion box algorithm in S3 is to place a space box in the point cloud data space. The expansion box algorithm adopts automatic expansion from small to large when placing the expansion box. The space box includes a door and window expansion box and a wall expansion box, and supports specified expansion in the horizontal and vertical directions. The door and window expansion box first expands to generate a door and window model, and then the wall expansion box is finally expanded to generate a three-dimensional model of the apartment room. During the expansion process, the wall expansion box automatically passes through the marked water and electricity points. The wall expansion box performs shearing when encountering the marked door and window model, and preferentially performs plane fitting based on the indoor wall surface. The expansion box algorithm sets a pass rate threshold according to the point cloud density and automatically calculates the wall expansion boundary. The space box automatically expands and adapts to change shape according to the point cloud data, determines straight walls and curved walls according to the flatness and density of the point cloud data, and calculates the curvature of the curved wall.

4. The three-dimensional modeling method of point cloud data according to claim 1, characterized in that: The custom JSON format in S3 includes the following data structure: Wall: centerline starting point, end point and thickness; Doors and windows: center point coordinates, width, height; Cylinder: center point coordinates and length, width or radius; Beam body: centerline starting point, end point, width and height; Water and electricity point information: category attributes (water, electricity, heating), center point coordinates, width, and height.

5. The three-dimensional modeling method of point cloud data according to claim 1, characterized in that: The point cloud data scanned by the LiDAR device supports .las and .e57 formats.

6. A point cloud data processing system, characterized in that: include, a data entry module for executing item S1 of claim 1, inputting point cloud data scanned by the lidar device and panoramic images captured by the image acquisition device into the system; a pre-processing module, configured to perform octree point cloud tile processing on input point cloud data as described in item S1 of claim 1; a point cloud filtering module, configured to execute item S3 of claim 1, identifying and removing invalid interference points in the point cloud data; a three-dimensional modeling module, configured to execute item S4 of claim 1, and generate a surface and wireframe structure of a three-dimensional model based on a box expansion algorithm; A data output module, configured to execute the custom JSON format output as described in claim 4, including model information; A visualization display module for displaying the three-dimensional model generated by item S4 of claim 1, supporting switching between coloring, wireframe, and point cloud modes; The measurement module is used to mark the length and angle in the real environment on the point cloud or model, and is calculated based on the model data first, and supports automatic calibration in the horizontal and vertical directions; The editing module allows users to dynamically adjust the model plane position and supports numerical input and mouse dragging operations.

7. The point cloud data processing system according to claim 6, characterized in that: It also includes a hydropower point library module and a door and window model library. The data entry module is responsible for entering the panoramic image that matches the point cloud data into the system, and automatically identifying and matching the hydropower points in the hydropower point library and the door and window models in the door and window model library based on the image attributes obtained from the panoramic image. The three-dimensional modeling module automatically marks and penetrates the hydropower points based on the hydropower points and door and window models identified by the data entry module, performs shearing on the door and window models, and generates a three-dimensional model using a box expansion algorithm.

8. The three-dimensional modeling method and point cloud data processing system according to claim 6, characterized in that: The measurement module has the ability to eliminate interference from small objects on the ground to improve the accuracy of model adjustment.

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