Interactive point cloud data editing method on mobile terminal

Abstract

The invention discloses an interactive point cloud data editing method on a mobile terminal, and relates to the technical field of point cloud data processing, and the method comprises the steps: collecting point cloud data in a transformer substation through a handheld laser radar, carrying out the primary processing of the point cloud data through the mobile terminal, and segmenting the point cloud data through a fragmentation algorithm; transmitting the fragmented point cloud data to an edge computing device through a high-speed wireless network, performing noise filtering on the point cloud data on the edge computing device by using an adaptive learning algorithm, and dynamically adjusting filtering parameters; on edge computing equipment, geometric feature extraction is carried out on the point cloud data subjected to noise filtering, and geometric feature points are extracted and compressed; the mobile terminal receives the compressed geometric feature point data returned from the edge computing device, the point cloud data is decompressed and reconstructed through an inverse sparse matrix decomposition algorithm, and a user can perform interactive editing operation through the mobile terminal; and the user adjusts the display view angle of the point cloud model through gesture operation.

Description

Technical Field

[0001] The present invention relates to the technical field of point cloud data processing, and in particular to an interactive point cloud data editing method on a mobile terminal. Background Art

[0002] With the rapid development of 3D scanning technology and sensor technology, point cloud data collection and processing technology has been widely used in many fields. Point cloud data is an important way to express 3D spatial information and is widely used in building information modeling (BIM), autonomous driving, virtual reality, geographic information system (GIS) and industrial inspection. In particular, the advancement of handheld laser radar (LiDAR) technology has made it possible to quickly obtain high-precision 3D point cloud data in complex environments. In the maintenance and management of power systems, the demand for 3D modeling of some key infrastructure in substations has become increasingly prominent. Point cloud data can clearly express the spatial position, size and relationship of equipment. However, due to the large amount of point cloud data, many noise points and serious data redundancy, how to efficiently process and edit point cloud data and realize real-time interactive operation on mobile terminals has become an important issue in the current technological development.

[0003] Existing point cloud data processing technologies are mainly concentrated on desktop computing platforms, and massive point cloud data are processed through high-performance computing resources. However, with the improvement of mobile device performance and the popularization of 5G communication networks, more application scenarios have begun to turn to mobile terminals. However, existing technologies have many limitations when extending point cloud data processing to mobile terminals, which are mainly reflected in the following aspects: First, the computing resources of mobile terminals are limited and cannot efficiently process large-scale point cloud data; second, the noise filtering and compression algorithms of point cloud data are relatively complex, and existing methods are difficult to achieve real-time requirements on mobile terminals; third, existing technologies fail to fully utilize the advantages of edge computing during data transmission, resulting in low efficiency of data interaction between mobile terminals and servers, which cannot meet the needs of real-time operations. In addition, in the process of editing point cloud data, how to achieve rapid adjustment and precise annotation of three-dimensional models through intuitive interactive methods (such as gesture operations) is also a difficult problem that needs to be solved urgently by current technologies. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an interactive point cloud data editing method on a mobile terminal to solve the problem that the noise filtering and compression algorithms of point cloud data are relatively complex and the existing methods are difficult to achieve real-time requirements on mobile terminals.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an interactive point cloud data editing method on a mobile terminal, comprising:

[0008] Use handheld laser radar to collect point cloud data in the substation, perform preliminary processing on the point cloud data through the mobile terminal, and use the slicing algorithm to segment the point cloud data;

[0009] The segmented point cloud data is transmitted to the edge computing device via a high-speed wireless network, and the point cloud data on the edge computing device is filtered using an adaptive learning algorithm to dynamically adjust the filtering parameters.

[0010] On the edge computing device, geometric feature extraction is performed on the noise-filtered point cloud data, and the geometric feature points are extracted and compressed;

[0011] The mobile terminal receives the compressed geometric feature point data returned from the edge computing device, decompresses and reconstructs the point cloud data through the inverse sparse matrix decomposition algorithm, and obtains complete three-dimensional point cloud data;

[0012] The decompressed and reconstructed point cloud data is displayed in real time on the mobile terminal, and the user can perform interactive editing operations through the mobile terminal;

[0013] Users adjust the display perspective of the point cloud model through gestures, and use editing tools to perform distance measurements and area annotation operations.

[0014] As a preferred solution of the interactive point cloud data editing method on the mobile terminal of the present invention, the point cloud data is collected in the substation using a handheld laser radar, the point cloud data is preliminarily processed by the mobile terminal, and the point cloud data is segmented using a slicing algorithm. The specific steps are:

[0015] Use handheld LiDAR devices to perform 3D spatial modeling in substations. LiDAR measures the 3D coordinates of objects by emitting laser beams and receiving reflected signals, generating high-density point cloud data.

[0016] The octree slicing algorithm based on spatial position is used to partition the collected point cloud data according to the spatial coordinates to generate multiple slices. The volume of each slice after segmentation is obtained, and the expression is:

[0017]

[0018] Among them, V p It represents the volume of the pth slice after n splits, p is the index variable, n represents the number of splits, and V0 represents the initial volume;

[0019] The initially processed point cloud data segments are transmitted to the edge computing device via the high-speed wireless network 5G.

[0020] As a preferred solution of the interactive point cloud data editing method on the mobile terminal of the present invention, the segmented point cloud data is transmitted to the edge computing device through a high-speed wireless network, and an adaptive learning algorithm is used to filter the noise of the point cloud data on the edge computing device, and the filtering parameters are dynamically adjusted, specifically,

[0021] Adaptive filtering algorithm is used to filter out noise points in the acquisition process in real time. The expression is:

[0022]

[0023] Among them, P(t) represents the probability of valid data at time t, P0 represents the validity probability of the initial point, e represents a natural constant, β represents a filtering parameter, d(t) represents the distance between the current point and its neighboring points, and d0 represents the distance threshold;

[0024] On the edge device, the adaptive learning algorithm optimizes the noise filtering parameters through multiple iterations. After each iteration, the adaptive learning algorithm gradually updates the filtering parameter β by analyzing the geometric distribution of each point in the point cloud data and its neighboring points.

[0025] Based on the initial conditions of the acquisition environment, the initial filtering parameters β and d0 are set, and the first filtering is performed;

[0026] After the initial filtering, the neighborhood distribution of the remaining point cloud data is analyzed to detect whether there are still abnormal points;

[0027] According to the distribution of noise points, the expression of gradually increasing or decreasing the filtering parameter β is:

[0028]

[0029] Among them, β n+1 represents the filtering parameter for the n+1th iteration, β n represents the filtering parameter of the nth iteration, γ represents the adjustment step size, N represents the total number of points in the point cloud data, and T represents the threshold of valid points.

[0030] As a preferred solution of the interactive point cloud data editing method on the mobile terminal of the present invention, wherein: on the edge computing device, geometric feature extraction is performed on the noise-filtered point cloud data, and the geometric feature points are extracted and compressed. The specific steps are:

[0031] The point cloud data after noise filtering is used with geometric feature descriptors to extract geometric feature points. The expression is:

[0032]

[0033] Among them, G i represents the geometric eigenvalue of the i-th point cloud data, j is the index variable, o represents the number of neighborhood points, d ij represents the distance between point i and its neighboring point j, f i and f j Represents the normal vector between point i and its neighboring point j.

[0034] As a preferred solution of the interactive point cloud data editing method on the mobile terminal of the present invention, the mobile terminal receives the compressed geometric feature point data returned from the edge computing device, decompresses and reconstructs the point cloud data through an inverse sparse matrix decomposition algorithm, and obtains complete three-dimensional point cloud data, including:

[0035] After feature extraction is completed, the edge computing device performs sparse matrix decomposition and data compression based on the geometric distribution of feature points;

[0036] On the edge device, the point cloud data is organized into a sparse matrix M, and the expression of the sparse matrix M is:

[0037]

[0038] Among them, x N Indicates the horizontal coordinate of the Nth point, y N Indicates the ordinate of the Nth point, z N Represents the height coordinate of the Mth point, where N is the total number of points in the point cloud data;

[0039] Use sparse singular value decomposition SVD to compress point cloud data, the expression is:

[0040] M=UDV T ;

[0041] Among them, U is an N×k matrix containing left singular vectors, D is a k×k diagonal matrix containing the singular values ​​of the matrix, and V T It is a k×3 matrix, where k represents the compressed dimension;

[0042] The compressed dimension k is calculated as follows:

[0043]

[0044] Among them, C represents the scene complexity factor, log represents the logarithmic function, and q i represents the number of neighboring points of the i-th point within the radius r, V r Represents the volume of the spherical neighborhood corresponding to radius r.

[0045] As a preferred solution of the interactive point cloud data editing method on the mobile terminal of the present invention, wherein: the decompressed and reconstructed point cloud data is displayed in real time on the mobile terminal, and the user can perform interactive editing operations through the mobile terminal. The specific steps are:

[0046] The point cloud data compressed by sparse matrix decomposition is transmitted to the mobile terminal via a wireless network;

[0047] After receiving the compressed data, the mobile terminal decompresses and reconstructs the point cloud data through the inverse sparse matrix decomposition algorithm to restore the complete three-dimensional point cloud data. The compression rate is dynamically adjusted according to C. The expression is:

[0048] M′=UDV T ;

[0049] Among them, M′ represents the decompressed point cloud data matrix, which represents the approximate reconstruction result of the original point cloud data.

[0050] As a preferred solution of the interactive point cloud data editing method on the mobile terminal of the present invention, the user adjusts the display viewing angle of the point cloud model through gesture operation, and performs distance measurement and area marking operations through editing tools, specifically,

[0051] After reconstruction, the mobile terminal uses WebGL for real-time rendering, and the user can perform various interactive operations through the touch screen of the mobile terminal. The user can perform various editing operations on the point cloud data through the editing tools on the mobile terminal;

[0052] Users can perform various interactive operations through the touch screen of the mobile terminal, including:

[0053] Rotation operation: Users can rotate the point cloud model by sliding with one finger and adjust the viewing angle to view 3D data from different angles;

[0054] Zoom operation: Users can adjust the size of the point cloud model and zoom in or out locally by using a two-finger zoom gesture;

[0055] Pan operation: Users can pan the point cloud model by sliding with two fingers to view different model areas on the display of the mobile terminal;

[0056] Perspective reset: By double-clicking the screen, the system can automatically reset the point cloud model perspective to the initial state;

[0057] Users can use the editing tools on the mobile terminal to perform various editing operations on point cloud data, including:

[0058] Selection of point cloud data: Users can select any point or area in the point cloud model by touching the screen, and the system will highlight the selected point cloud data;

[0059] Deletion of point cloud data: After the user selects a specific point or area in the point cloud model, the user can delete the unnecessary data points;

[0060] Marking of point cloud data: Users can place markers at any location in the point cloud model to record or annotate information at a specific location. The markers can be associated with external data.

[0061] Measurement of point cloud data: Users can select any two points and calculate the Euclidean distance between the two points to achieve distance measurement.

[0062] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the interactive point cloud data editing method on a mobile terminal as described in the first aspect of the present invention is implemented.

[0063] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the interactive point cloud data editing method on a mobile terminal as described in the first aspect of the present invention is implemented.

[0064] The beneficial effects of the present invention are as follows: the present invention collects point cloud data through a handheld laser radar and uses an octree slicing algorithm for preliminary processing, thereby reducing the computing burden of the mobile terminal; secondly, the noise filtering parameters are dynamically adjusted through an adaptive learning algorithm to effectively remove noise, retain key points, and improve data quality; subsequently, sparse matrix decomposition technology is used for dynamic compression, and the compression rate is adjusted in combination with the complexity of the scene, which significantly reduces the amount of data and ensures information integrity; after transmission through a wireless network, the mobile terminal can efficiently decompress the point cloud data and reconstruct the three-dimensional model; users can interact in real time through WebGL and use dynamically adjusted rendering resolution to ensure smooth display; in addition, the system enhances practicality through functions such as distance measurement; the overall technical solution improves the processing efficiency, transmission speed and user interaction experience of large-scale point cloud data, and is particularly suitable for three-dimensional space modeling and data management in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0066] Figure 1 This is a flowchart of the interactive point cloud data editing method on a mobile terminal in Example 1.

[0067] Figure 2 This is a flowchart of decompression and reconstruction of the mobile terminal in Example 1. DETAILED DESCRIPTION

[0068] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0069] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0070] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0071] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides an interactive point cloud data editing method on a mobile terminal, comprising the following steps:

[0072] S1. Use handheld laser radar to collect point cloud data in the substation, perform preliminary processing on the point cloud data through the mobile terminal, and use the slicing algorithm to segment the point cloud data;

[0073] Use handheld LiDAR devices to perform 3D spatial modeling in substations. LiDAR measures the 3D coordinates of objects by emitting laser beams and receiving reflected signals, generating high-density point cloud data.

[0074] During the data collection process, the occlusion of power equipment, reflective surfaces and light conditions will cause noise and abnormal points. Therefore, the data collection process must be optimized to ensure the accuracy of the data.

[0075] Due to the limited computing and storage capabilities of mobile terminals, the overall size of point cloud data may exceed the processing capabilities of mobile terminals. Therefore, the data must be processed in slices to reduce the computing burden of mobile terminals.

[0076] The octree slicing algorithm based on spatial position is used to partition the collected point cloud data according to the spatial coordinates to generate multiple slices. The volume of each slice after segmentation is obtained, and the expression is:

[0077]

[0078] Among them, V p It represents the volume of the pth slice after n splits, p is the index variable, n represents the number of splits, and V0 represents the initial volume;

[0079] The initially processed point cloud data is segmented and transmitted to the edge computing device via the high-speed wireless network 5G;

[0080] During the transmission process, each slice should contain complete 3D point cloud coordinate information and the validity mark of each point to ensure that the edge device can further process the point cloud data.

[0081] S2. Transmit the segmented point cloud data to the edge computing device via a high-speed wireless network, use an adaptive learning algorithm to filter the noise of the point cloud data on the edge computing device, and dynamically adjust the filtering parameters; on the edge computing device, extract geometric features of the noise-filtered point cloud data, extract geometric feature points, and compress them;

[0082] Adaptive filtering algorithm is used to filter out obvious noise points in the acquisition process in real time, that is, reflection interference. The expression is:

[0083]

[0084] Among them, P(t) represents the probability of valid data at time t, P0 represents the validity probability of the initial point, which is obtained by collecting in an open area without interference, e represents a natural constant, β represents a filtering parameter, which can be set according to demand. The more complex the environment, the larger the value of α is to accelerate the attenuation of noise points, and vice versa. d(t) represents the distance between the current point and its neighboring points, and d0 represents the distance threshold. Points exceeding this threshold will be considered invalid points.

[0085] The value range of the valid data probability P(t) is [0,1]. When the value is larger, it means that the point is a valid point and can be retained in the subsequent processing link; when the value is smaller, it means that the point is a noise point and should be removed. This formula dynamically adjusts the validity probability of point cloud data to ensure the noise filtering effect in complex environments.

[0086] On the edge device, the adaptive learning algorithm optimizes the noise filtering parameters through multiple iterations. After each iteration, the adaptive learning algorithm analyzes the geometric distribution of each point in the point cloud data and its neighborhood points, and gradually updates the filtering parameter β to improve the filtering efficiency.

[0087] Based on the initial conditions of the acquisition environment (such as noise expectation, equipment occlusion, and light reflection), set the initial parameters β and d0, and perform the first filtering;

[0088] After the initial filtering, the neighborhood distribution of the remaining point cloud data is analyzed to detect whether there are still abnormal points (such as isolated points or points with too large distance);

[0089] According to the distribution of noise points, the filtering parameter β is gradually increased or decreased to enhance or weaken the suppression effect of noise points. The expression is:

[0090]

[0091] Among them, β n+1 represents the filtering parameter for the n+1th iteration, β n represents the filtering parameter of the nth iteration, γ represents the adjustment step size, N represents the total number of points in the point cloud data, and T represents the threshold of valid points;

[0092] The point cloud data after noise filtering is used with geometric feature descriptors to extract geometric feature points. These feature points will be used for subsequent 3D reconstruction and data compression. The expression is:

[0093]

[0094] Among them, G i Represents the geometric eigenvalue of the i-th point cloud data. The geometric eigenvalue includes the distance between neighboring points and the similarity between normal vectors. j is the index variable, o represents the number of neighboring points, and d ij represents the distance between point i and its neighboring point j, f i and f j Represents the normal vector of point i and its neighboring point j;

[0095] Among them, G i It is a positive number, and the larger the value, the higher the importance of the point;

[0096] In order to improve transmission efficiency, data compression technology is used to perform lightweight compression on the sliced ​​data. The compression algorithm is dynamically adjusted according to the sparsity of the point cloud data to ensure the efficiency and integrity of data transmission.

[0097] S3. The mobile terminal receives the compressed geometric feature point data returned from the edge computing device, decompresses and reconstructs the point cloud data through an inverse sparse matrix decomposition algorithm, and obtains complete three-dimensional point cloud data;

[0098] After feature extraction is completed, the edge computing device performs sparse matrix decomposition and data compression based on the geometric distribution of feature points, and uses the geometric eigenvalues ​​as the input of the compression algorithm to ensure that only the most informative key points are retained to reduce the burden of data transmission and storage;

[0099] On the edge device, the point cloud data is organized into a sparse matrix M. Point cloud data is usually a collection of three-dimensional space coordinates. Each point is represented by (x, y, z) coordinates. The distance between points can be represented by the neighborhood relationship matrix. Therefore, the point cloud data can be combined into a sparse matrix M. Its sparsity comes from the sparse distribution characteristics of the point cloud. The expression of the sparse matrix M is:

[0100]

[0101] Among them, x N Indicates the horizontal coordinate of the Nth point, y N Indicates the ordinate of the Nth point, z N Indicates the height coordinate of the Nth point, where N is the total number of points in the point cloud data;

[0102] Use sparse singular value decomposition SVD to compress point cloud data, the expression is:

[0103] M=UDV T ;

[0104] Among them, U is an N×k matrix containing left singular vectors, D is a k×k diagonal matrix containing the singular values ​​of the matrix, and V T It is a k×3 matrix, where k represents the compressed dimension;

[0105] The core innovation of this invention is the sparse matrix decomposition technology and dynamic compression adjustment according to the scene complexity. First, sparse matrix decomposition SVD has outstanding advantages in point cloud data compression. SVD decomposition can represent point cloud data as UDV T , where U and V T are the singular vector matrices, and D is the singular value matrix. This decomposition process can effectively extract the principal components of the point cloud data, retain the geometric feature points, and reduce redundant data. This is particularly useful when the computing power of mobile terminals is limited because it can significantly reduce the burden of data processing and storage;

[0106] The compressed dimension k is calculated as follows:

[0107]

[0108] Among them, C represents the scene complexity factor. The value of C can be calculated based on the number of objects in the scene, the complexity of the geometric structure, and the occlusion rate. The larger the value, the more complex the scene is and the lower the compression rate is. On the contrary, the lower the compression rate is, log represents the logarithmic function, q i represents the number of neighboring points of the i-th point within the radius r, V r Represents the volume of the spherical neighborhood corresponding to radius r;

[0109] The present invention dynamically adjusts the compression ratio through the scene complexity factor C, which has a significant technical breakthrough. The prior art usually adopts a static compression ratio, which cannot be adjusted according to the scene complexity, resulting in the loss of key information in complex scenes and the waste of bandwidth in simple scenes. The present invention, through the introduction of C, can adjust the compression dimension kkk according to the number of objects in the scene, the complexity of the geometric structure and the occlusion rate, thereby achieving adaptive compression;

[0110] The point cloud data compressed by sparse matrix decomposition is transmitted to the mobile terminal via a wireless network;

[0111] After receiving the compressed data, the mobile terminal decompresses and reconstructs the point cloud data through the inverse sparse matrix decomposition algorithm to restore the complete three-dimensional point cloud data. The compression rate is dynamically adjusted according to C. The expression is:

[0112] M′=UDV T ;

[0113] Wherein, M′ represents the decompressed point cloud data matrix, which represents the approximate reconstruction result of the original point cloud data, M≠M′. Since the dimension k of the matrix is ​​reduced during the compression process, there is an error between M′ and the sparse matrix M. The compression accuracy depends on the scene complexity factor C and the compression dimension k.

[0114] The present invention solves the problems of inflexible compression rate, low data transmission efficiency and waste of computing resources in the prior art through sparse matrix decomposition and dynamic adjustment of scene complexity. This method is particularly suitable for processing large-scale point cloud data on mobile terminals, which significantly improves processing efficiency and interactive experience.

[0115] S4. The decompressed and reconstructed point cloud data is displayed in real time on the mobile terminal, and the user can perform interactive editing operations through the mobile terminal; the user adjusts the display viewing angle of the point cloud model through gesture operations, and performs distance measurement and area annotation operations through editing tools.

[0116] After reconstruction, the mobile terminal uses WebGL for real-time rendering, and users can interact with the point cloud data in real time through rotation, zooming, translation and distance measurement;

[0117] Users can perform various interactive operations through the touch screen of the mobile terminal, including:

[0118] Rotation operation: Users can rotate the point cloud model by sliding with one finger and adjust the viewing angle to view 3D data from different angles;

[0119] Zoom operation: Users can adjust the size of the point cloud model and zoom in or out locally by using a two-finger zoom gesture;

[0120] Pan operation: Users can pan the point cloud model by sliding with two fingers to view different model areas on the display of the mobile terminal;

[0121] Perspective reset: By double-clicking the screen, the system can automatically reset the point cloud model perspective to the initial state;

[0122] Users can use the editing tools on the mobile terminal to perform various editing operations on point cloud data, including:

[0123] Selection of point cloud data: Users can select any point or area in the point cloud model by touching the screen, and the system will highlight the selected point cloud data;

[0124] Deletion of point cloud data: After the user selects a specific point or area in the point cloud model, the user can delete the unnecessary data points;

[0125] Marking of point cloud data: Users can place markers at any location in the point cloud model to record or annotate information at a specific location. The markers can be associated with external data.

[0126] Measurement of point cloud data: Users can select any two points and calculate the Euclidean distance between the two points to achieve distance measurement. The calculation formula is:

[0127]

[0128] Where d represents the distance between two points, (x1, y1, z1) and (x2, y2, z2) represent the three-dimensional coordinates of the two selected points respectively;

[0129] Area annotation: Users can use gestures to select an area of ​​the point cloud model and use the text box tool to add annotation information to the area;

[0130] The standard formula for calculating the Euclidean distance is widely used to measure the straight-line distance between two points in three-dimensional space. In the present invention, the user can select any two points and calculate their distance to achieve accurate measurement of certain objects or areas in the point cloud. Specifically, (x1, y1, z1) and (x2, y2, z2) represent the three-dimensional coordinates of the two points selected by the user, which are directly derived from the point cloud data. In the application scenarios of point cloud data, such as three-dimensional modeling of substations or measurement of complex structures, it is very practical to be able to measure distances in real time and accurately. For example, users can use this function to measure the distance between devices, thereby assisting maintenance and overhaul decisions. Therefore, the application of the Euclidean distance formula here is not only reasonable, but also provides a key user interaction function;

[0131] In order to ensure that the rendering resolution can be adaptively adjusted under different scene complexity conditions, a dynamic rendering optimization formula is constructed according to the scene complexity factor C, and the expression is:

[0132]

[0133] Among them, R represents the current rendering resolution and R0 represents the maximum resolution.

[0134] This embodiment further provides a computer device, which is applicable to the case of the interactive point cloud data editing method on a mobile terminal, comprising: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement the interactive point cloud data editing method on the mobile terminal proposed in the above embodiment;

[0135] The key innovations of the present invention are: (1) dynamically adjusting the rendering resolution to adapt to the scene complexity; (2) providing multiple user interaction modes to improve the user experience. Compared with the static rendering resolution setting in the prior art, the present invention realizes dynamic adjustment by introducing the scene complexity factor. Specifically, if the scene is relatively simple, C is low, and the rendering resolution is close to the maximum value, the user can see higher-precision point cloud data; if the scene is complex, the rendering resolution will automatically decrease to ensure smooth operation. Through this adaptive adjustment mechanism, the present invention effectively solves the problem of limited computing power of mobile terminals and large changes in scene complexity.

[0136] In terms of user interaction, operations such as rotation, scaling, translation, and distance measurement are all implemented through intuitive gestures. Combined with the real-time rendering function of WebGL, users can freely explore 3D point cloud data and gain an immersive experience. These operations are also common in other 3D data visualization applications, but in this invention, combined with scene complexity and dynamic rendering technology, these interactive operations are still smooth in complex scenes, ensuring the consistency of user experience;

[0137] To sum up, the dynamic rendering optimization mechanism and various user interaction methods in the present invention are not only reasonable but also technically innovative. In particular, the design of adjusting the rendering resolution according to the complexity of the scene can effectively deal with data processing and display problems in complex scenes, thereby improving the system performance and user experience.

[0138] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0139] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for implementing interactive point cloud data editing on a mobile terminal as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, disk or optical disk.

[0140] In summary, the present invention reduces the computational burden of mobile terminals by: using a handheld laser radar to collect point cloud data and using an octree slicing algorithm for preliminary processing; secondly, dynamically adjusting noise filtering parameters through an adaptive learning algorithm to effectively remove noise, retain key points, and improve data quality; subsequently, using sparse matrix decomposition technology for dynamic compression, and adjusting the compression rate based on scene complexity, which significantly reduces the amount of data and ensures information integrity; after transmission through a wireless network, the mobile terminal can efficiently decompress the point cloud data and reconstruct a three-dimensional model; users can interact in real time through WebGL, and use dynamically adjusted rendering resolution to ensure smooth display; in addition, the system enhances practicality through functions such as distance measurement; the overall technical solution improves the processing efficiency, transmission speed, and user interaction experience of large-scale point cloud data, and is particularly suitable for three-dimensional space modeling and data management in complex environments.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An interactive point cloud data editing method on a mobile terminal, characterized in that: include, Use handheld laser radar to collect point cloud data in the substation, perform preliminary processing on the point cloud data through the mobile terminal, and use the slicing algorithm to segment the point cloud data; The segmented point cloud data is transmitted to the edge computing device via a high-speed wireless network, and the point cloud data on the edge computing device is filtered using an adaptive learning algorithm to dynamically adjust the filtering parameters. On the edge computing device, geometric feature extraction is performed on the noise-filtered point cloud data, and the geometric feature points are extracted and compressed; The mobile terminal receives the compressed geometric feature point data returned from the edge computing device, decompresses and reconstructs the point cloud data through the inverse sparse matrix decomposition algorithm, and obtains complete three-dimensional point cloud data; The decompressed and reconstructed point cloud data is displayed in real time on the mobile terminal, and the user can perform interactive editing operations through the mobile terminal; Users adjust the display perspective of the point cloud model through gestures, and use editing tools to perform distance measurements and area annotation operations.

2. The interactive point cloud data editing method on a mobile terminal according to claim 1, characterized in that: The method uses a handheld laser radar to collect point cloud data in a substation, performs preliminary processing on the point cloud data through a mobile terminal, and uses a slicing algorithm to segment the point cloud data. The specific steps are: Use handheld LiDAR devices to perform 3D spatial modeling in substations. LiDAR measures the 3D coordinates of objects by emitting laser beams and receiving reflected signals, generating high-density point cloud data. The octree slicing algorithm based on spatial position is used to partition the collected point cloud data according to the spatial coordinates to generate multiple slices. The volume of each slice after segmentation is obtained, and the expression is: Among them, V p It represents the volume of the pth slice after n splits, p is the index variable, n represents the number of splits, and V0 represents the initial volume; The initially processed point cloud data segments are transmitted to the edge computing device via the high-speed wireless network 5G.

3. The interactive point cloud data editing method on a mobile terminal according to claim 2, characterized in that: The segmented point cloud data is transmitted to the edge computing device via a high-speed wireless network, and an adaptive learning algorithm is used to filter the noise of the point cloud data on the edge computing device, and the filtering parameters are dynamically adjusted, specifically, Adaptive filtering algorithm is used to filter out noise points in the acquisition process in real time. The expression is: Where P(t) represents the probability of valid data at time t, P0 represents the validity probability of the initial point, e represents a natural constant, β represents a filtering parameter, d(r) represents the distance between the current point and its neighboring points, and d0 represents the distance threshold; On the edge device, the adaptive learning algorithm optimizes the noise filtering parameters through multiple iterations. After each iteration, the adaptive learning algorithm gradually updates the filtering parameter β by analyzing the geometric distribution of each point in the point cloud data and its neighboring points. Based on the initial conditions of the acquisition environment, the initial filtering parameters β and d0 are set, and the first filtering is performed; After the initial filtering, the neighborhood distribution of the remaining point cloud data is analyzed to detect whether there are still abnormal points; According to the distribution of noise points, the expression of gradually increasing or decreasing the filtering parameter β is: Among them, β n+1 represents the filtering parameter for the n+1th iteration, β n represents the filtering parameter of the nth iteration, γ represents the adjustment step size, N represents the total number of points in the point cloud data, and T represents the threshold of valid points.

4. The interactive point cloud data editing method on a mobile terminal according to claim 3, characterized in that: The method of performing geometric feature extraction on the noise-filtered point cloud data on the edge computing device, extracting geometric feature points and compressing them is as follows: The point cloud data after noise filtering is used with geometric feature descriptors to extract geometric feature points. The expression is: Among them, G i represents the geometric eigenvalue of the i-th point cloud data, j is the index variable, o represents the number of neighborhood points, d ij represents the distance between point i and its neighboring point j, f i and f j Represents the normal vector between point i and its neighboring point j.

5. The interactive point cloud data editing method on a mobile terminal according to claim 4, characterized in that: The mobile terminal receives the compressed geometric feature point data returned from the edge computing device, decompresses and reconstructs the point cloud data through an inverse sparse matrix decomposition algorithm, and obtains complete three-dimensional point cloud data, including: After feature extraction is completed, the edge computing device performs sparse matrix decomposition and data compression based on the geometric distribution of feature points; On the edge device, the point cloud data is organized into a sparse matrix M, and the expression of the sparse matrix M is: Among them, x N Indicates the horizontal coordinate of the Nth point, y N Indicates the ordinate of the Nth point, z N Indicates the height coordinate of the Nth point, where N is the total number of points in the point cloud data; Use sparse singular value decomposition SVD to compress point cloud data, the expression is: M=UDV T ; Among them, U is an N×k matrix containing left singular vectors, D is a k×k diagonal matrix containing the singular values ​​of the matrix, and V T It is a k×3 matrix, where k represents the compressed dimension; The compressed dimension k is calculated as follows: Among them, C represents the scene complexity factor, log represents the logarithmic function, and q i represents the number of neighboring points of the i-th point within the radius r, V r Represents the volume of the spherical neighborhood corresponding to radius r.

6. The interactive point cloud data editing method on a mobile terminal according to claim 5, characterized in that: The decompressed and reconstructed point cloud data is displayed in real time on the mobile terminal, and the user can perform interactive editing operations through the mobile terminal. The specific steps are: The point cloud data compressed by sparse matrix decomposition is transmitted to the mobile terminal via a wireless network; After receiving the compressed data, the mobile terminal decompresses and reconstructs the point cloud data through the inverse sparse matrix decomposition algorithm to restore the complete three-dimensional point cloud data. The compression rate is dynamically adjusted according to C. The expression is: M′=UDV T ; Among them, M′ represents the decompressed point cloud data matrix, which represents the approximate reconstruction result of the original point cloud data.

7. The interactive point cloud data editing method on a mobile terminal according to claim 6, characterized in that: The user adjusts the display viewing angle of the point cloud model through gesture operations, and performs distance measurement and area annotation operations through editing tools, specifically, After reconstruction, the mobile terminal uses WebGL for real-time rendering, and the user can perform various interactive operations through the touch screen of the mobile terminal. The user can perform various editing operations on the point cloud data through the editing tools on the mobile terminal; The user performs various interactive operations through the touch screen of the mobile terminal, specifically including: Rotation operation: Users can rotate the point cloud model by sliding with one finger and adjust the viewing angle to view 3D data from different angles; Zoom operation: Users can adjust the size of the point cloud model and zoom in or out locally by using a two-finger zoom gesture; Pan operation: Users can pan the point cloud model by sliding with two fingers to view different model areas on the display of the mobile terminal; Perspective reset: By double-clicking the screen, the system can automatically reset the point cloud model perspective to the initial state; The user can perform various editing operations on the point cloud data through the editing tools on the mobile terminal, including: Selection of point cloud data: Users can select any point or area in the point cloud model by touching the screen, and the system will highlight the selected point cloud data; Deletion of point cloud data: After the user selects a specific point or area in the point cloud model, the user can delete the unnecessary data points; Marking of point cloud data: Users can place markers at any location in the point cloud model to record or annotate information at a specific location. The markers can be associated with external data. Measurement of point cloud data: Users can select any two points and calculate the Euclidean distance between the two points to achieve distance measurement.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the interactive point cloud data editing method on a mobile terminal according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the interactive point cloud data editing method on a mobile terminal according to any one of claims 1 to 7 are implemented.

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