A partition encryption method for three-dimensional point cloud data and related equipment

Abstract

The application relates to the technical field of point cloud data encryption, and provides a partition encryption method for three-dimensional point cloud data and related equipment, which comprises the following steps: performing spatial division on target three-dimensional point cloud data to obtain a plurality of sub-point cloud data; calculating the importance score of each sub-point cloud data; determining the importance level of each sub-point cloud data according to all the importance scores; encrypting each sub-point cloud data based on the importance level of each sub-point cloud data to obtain the encrypted sub-point cloud data corresponding to each sub-point cloud data; and integrating all the encrypted sub-point cloud data to obtain the final encrypted point cloud data of the target three-dimensional point cloud data. The method can improve the security and geometric stability of three-dimensional point cloud encryption.

Description

Technical Field

[0001] This application relates to the field of point cloud data encryption technology, and in particular to a method and related equipment for partitioning and encrypting three-dimensional point cloud data. Background Technology

[0002] 3D point cloud data, as an important form of representing the spatial geometry and structural features of objects, is widely used in high-precision map construction, autonomous driving, Building Information Modeling (BIM), and the digitization of cultural heritage. With the continuous development of 3D sensing technologies such as LiDAR (Light Detection and Ranging), structured light, and stereo vision, the acquisition of point cloud data has become more efficient and widespread, and its accuracy and scale have significantly improved. However, because point cloud data typically contains rich spatial information and sensitive geographical or structural details, it faces high security risks during data transmission, sharing, and storage, making it vulnerable to attacks such as theft, tampering, or illegal analysis. Therefore, how to effectively encrypt and protect point cloud data to ensure its confidentiality and integrity during transmission and use has become a critical issue that urgently needs to be addressed.

[0003] Compared to the encryption of traditional image or text data, point cloud data is characterized by high dimensionality, disorder, and complex local structure, resulting in poor encryption security and geometric stability of 3D point cloud data. Summary of the Invention

[0004] This application provides a method and related equipment for partitioning and encrypting three-dimensional point cloud data, which can solve the problems of poor encryption security and geometric stability of three-dimensional point cloud data.

[0005] In a first aspect, embodiments of this application provide a method for partitioning and encrypting three-dimensional point cloud data, the method comprising:

[0006] The target 3D point cloud data is spatially divided to obtain multiple sub-point cloud data;

[0007] For each sub-point cloud data, calculate the importance score of the sub-point cloud data;

[0008] Based on all importance scores, determine the importance level of each sub-point cloud data;

[0009] Based on the importance level of each sub-point cloud data, each sub-point cloud data is encrypted to obtain the encrypted sub-point cloud data corresponding to each sub-point cloud data.

[0010] All encrypted sub-point cloud data are integrated to obtain the final encrypted point cloud data of the target 3D point cloud data.

[0011] Optionally, calculate the importance score of the sub-point cloud data, including:

[0012] Through the formula:

[0013] ;

[0014] Calculate the importance score of sub-point cloud data ;

[0015] in, This represents the volume of the spatial cube occupied by the sub-point cloud data. The mean squared error of the distribution of point cloud coordinate dimensions in the sub-point cloud data:

[0016] ;

[0017] ;

[0018] in, This represents the standard deviation of the x-coordinate distribution of the sub-point cloud data. This represents the standard deviation of the y-coordinate distribution of the sub-point cloud data. This represents the standard deviation of the z-coordinate distribution of the sub-point cloud data. This represents the maximum x-coordinate of the sub-point cloud data. Represents the minimum x-coordinate of the sub-point cloud data. This represents the maximum y-coordinate of the sub-point cloud data. This represents the minimum y-coordinate of the sub-point cloud data. This represents the maximum z-coordinate of the sub-point cloud data. This represents the minimum z-coordinate of the sub-point cloud data.

[0019] Optionally, the importance level can be the first importance level, the second importance level, or the third importance level;

[0020] Based on all importance scores, determine the importance level of each sub-point cloud data, including:

[0021] Multiple initial classification results are set; the initial classification results include the initial importance level of each sub-point cloud data, which can be the first importance level, the second importance level, or the third importance level;

[0022] For each initial classification result, a classification score is calculated based on the importance scores of all sub-point cloud data.

[0023] Each initial importance level in the initial grading result with the highest grading score is taken as the importance level of the corresponding sub-point cloud data.

[0024] Optionally, a classification score is calculated based on the importance scores of all sub-point cloud data to determine the initial classification result, including:

[0025] Through the formula:

[0026] ;

[0027] ;

[0028] ;

[0029] Calculate the grading score of the initial grading results ;

[0030] in, This represents the within-group variance. This represents the variance of the distribution of importance scores at the first importance level. This represents the variance of the distribution of importance scores for the second importance level. This represents the variance of the distribution of importance scores for the third importance level. This represents the variance between groups in the population. For the number of sub-point cloud data, This represents the number of sub-point cloud data points of the first importance level in the initial classification results. This represents the number of sub-point cloud data points of the second importance level in the initial classification results. This represents the number of sub-point cloud data points at the third importance level in the initial classification results. This represents the overall importance mean. This represents the mean importance score of the first importance level in the initial classification results. This represents the mean importance score of the second importance level in the initial grading results. This represents the mean importance score of the third importance level in the initial grading results.

[0031] Optionally, based on the importance level of each sub-point cloud data, each sub-point cloud data is encrypted to obtain encrypted sub-point cloud data corresponding to each sub-point cloud data, including:

[0032] For each sub-point cloud data, perform the following steps:

[0033] Determine the encryption equation for the sub-point cloud data based on its importance level;

[0034] The sub-point cloud data is encrypted using an encryption equation to obtain the encrypted sub-point cloud data corresponding to the sub-point cloud data.

[0035] Optionally, the encryption equation can be a one-dimensional chaotic mapping equation, a two-dimensional chaotic mapping equation, or a three-dimensional chaotic mapping equation.

[0036] Based on the importance level of the sub-point cloud data, determine the encryption equation for the sub-point cloud data, including:

[0037] When the importance level of the sub-point cloud data is the first importance level, the encryption equation is a one-dimensional chaotic mapping equation.

[0038] When the importance level of the sub-point cloud data is the second importance level, the encryption equation is a two-dimensional chaotic mapping equation;

[0039] When the importance level of the sub-point cloud data is the third importance level, the encryption equation is a three-dimensional chaotic mapping equation.

[0040] Secondly, embodiments of this application provide a partitioning encryption device for three-dimensional point cloud data, comprising:

[0041] The partitioning module is used to spatially partition the target 3D point cloud data to obtain multiple sub-point cloud data.

[0042] The calculation module is used to calculate the importance score of each sub-point cloud data separately.

[0043] The determination module is used to determine the importance level of each sub-point cloud data based on all importance scores;

[0044] The encryption module encrypts each sub-point cloud data based on its importance level, thus obtaining the encrypted sub-point cloud data corresponding to each sub-point cloud data.

[0045] The integration module is used to integrate all encrypted sub-point cloud data to obtain the final encrypted point cloud data of the target 3D point cloud data.

[0046] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned method for partitioning and encrypting three-dimensional point cloud data.

[0047] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for partitioning and encrypting three-dimensional point cloud data.

[0048] The above-mentioned solution in this application has the following beneficial effects:

[0049] In the embodiments of this application, the target 3D point cloud data is spatially divided to obtain multiple sub-point cloud data. Then, for each sub-point cloud data, an importance score is calculated. Finally, based on all importance scores, the importance level of each sub-point cloud data is determined. Then, based on the importance level of each sub-point cloud data, each sub-point cloud data is encrypted to obtain encrypted sub-point cloud data. Finally, all encrypted sub-point cloud data are integrated to obtain the final encrypted point cloud data of the target 3D point cloud data. Specifically, spatially dividing the 3D point cloud data, encrypting the sub-point cloud data, and then integrating them to obtain the final encrypted point cloud data avoids the geometric structure destruction caused by directly encrypting the entire point cloud, improving the geometric stability of the point cloud encryption. Encrypting according to the importance level of different sub-point cloud data enhances the complexity and encryption strength of the point cloud encryption, thereby improving the security of the 3D point cloud encryption.

[0050] Other beneficial effects of this application will be described in detail in the following detailed description section. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart illustrating a method for partitioning and encrypting 3D point cloud data according to an embodiment of this application;

[0053] Figure 2 This is a schematic diagram of point cloud data provided in an embodiment of this application;

[0054] Figure 3 A schematic diagram of the final encrypted point cloud data when the octree depth is 1, provided as an embodiment of this application;

[0055] Figure 4 A schematic diagram of the final encrypted point cloud data when the octree depth is 2, provided as an embodiment of this application;

[0056] Figure 5 This is a schematic diagram of point cloud decryption provided in an embodiment of this application;

[0057] Figure 6 A schematic diagram of the structure of a partition encryption device for three-dimensional point cloud data provided in an embodiment of this application;

[0058] Figure 7 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0059] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0060] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0061] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0062] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0063] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0064] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0065] To address the issues of poor encryption security and geometric stability in existing 3D point cloud data, this application provides a partitioned encryption method for 3D point cloud data. This method spatially divides the target 3D point cloud data into multiple sub-point cloud data. Then, for each sub-point cloud data, an importance score is calculated. Finally, based on all importance scores, the importance level of each sub-point cloud data is determined. Based on the importance level of each sub-point cloud data, each sub-point cloud data is encrypted, resulting in encrypted sub-point cloud data for each sub-point cloud data. Finally, all encrypted sub-point cloud data are integrated to obtain the final encrypted point cloud data of the target 3D point cloud data. Specifically, spatially dividing the 3D point cloud data, encrypting the sub-point cloud data, and then integrating them to obtain the final encrypted point cloud data avoids the geometric structure destruction caused by directly encrypting the entire point cloud, thus improving the geometric stability of the point cloud encryption. Encrypting based on the importance level of different sub-point cloud data enhances the complexity and encryption strength of the point cloud encryption, thereby improving the security of the 3D point cloud encryption.

[0066] The following is an exemplary description of the partition encryption method for 3D point cloud data provided in this application.

[0067] like Figure 1 As shown, the partitioning encryption method for 3D point cloud data provided in this application includes the following steps:

[0068] Step 11: Spatial division of the target 3D point cloud data to obtain multiple sub-point cloud data.

[0069] The aforementioned target 3D point cloud data is 3D point cloud data that needs to be encrypted.

[0070] For example, the target 3D point cloud data is stored in PLY format. Tools such as Open3D can be used to read the file header and data portion of the point cloud data file to obtain the 3D point cloud coordinate information.

[0071] In some embodiments of this application, a linear octree can be used to spatially partition the target 3D point cloud data to obtain multiple sub-point cloud data.

[0072] An octree is a hierarchical data structure that recursively divides a three-dimensional space into eight subspaces. It is widely used in applications such as computer graphics, geographic information systems, collision detection, voxel data representation, and point cloud data management.

[0073] A classic octree begins with an initial large cube covering the entire target space (usually called the root node, which is also the initial parent node). If the number of objects (such as points, polygons, or voxels) in this space exceeds a certain threshold, or if a finer representation of the space is required, then this cube is uniformly divided into eight child cubes of equal size. Each parent node is divided into eight child nodes, which correspond to the eight quadrants of the parent cube along the X, Y, and Z dimensions. The advantages of a classic octree are its intuitive spatial positioning and ease of geometric queries (such as finding all points within a certain region). The disadvantages are the need for a large number of pointers to maintain the tree structure, resulting in high memory overhead; and the complexity of tree traversal and maintenance.

[0074] To address the memory and access efficiency issues of classic octrees, linear octrees utilize the core idea of ​​Morton coding to implicitly represent the hierarchical structure of a three-dimensional space partition using a one-dimensional sequence (usually an array or list) without requiring explicit pointers.

[0075] For example, for point cloud data First, normalize the coordinates in the X, Y, and Z dimensions respectively:

[0076] ;

[0077] in, Then, based on the octree depth, obtain the integer voxel coordinates of each point. :

[0078] ;

[0079] in, This represents the floor function, which ultimately produces the Morton code for each point. The calculation is as follows:

[0080] ;

[0081] ;

[0082] in, For bitwise OR operation, This is a left shift operation. The () function "spreads out" the bits of an integer in a binary sequence, inserting zero bits, i.e. (v) converts v to binary Convert to .

[0083] After the above steps, for all points Given the depth of the octree, there is a corresponding Morton. Points falling within the same octagonal leaf node spatial block have the same corresponding code, thus completing the spatial partitioning and index construction of the point cloud data.

[0084] Step 12: Calculate the importance score for each sub-point cloud data.

[0085] The importance score described above describes the importance of sub-point cloud data within the overall target 3D point cloud data. The higher the importance score, the greater the importance.

[0086] Specifically, through the formula:

[0087] ;

[0088] Calculate the importance score of sub-point cloud data .

[0089] in, This represents the volume of the spatial cube occupied by the sub-point cloud data. The mean squared error of the distribution of point cloud coordinate dimensions in the sub-point cloud data:

[0090] ;

[0091] ;

[0092] in, This represents the standard deviation of the x-coordinate distribution of the sub-point cloud data. This represents the standard deviation of the y-coordinate distribution of the sub-point cloud data. This represents the standard deviation of the z-coordinate distribution of the sub-point cloud data. This represents the maximum x-coordinate of the sub-point cloud data. Represents the minimum x-coordinate of the sub-point cloud data. This represents the maximum y-coordinate of the sub-point cloud data. This represents the minimum y-coordinate of the sub-point cloud data. This represents the maximum z-coordinate of the sub-point cloud data. This represents the minimum z-coordinate of the sub-point cloud data.

[0093] Step 13: Determine the importance level of each sub-point cloud data based on all importance scores.

[0094] The above importance levels are first, second, or third importance levels. For example, first importance level corresponds to low importance, second importance level corresponds to medium importance, and third importance level corresponds to high importance.

[0095] In some embodiments of this application, the step of determining the importance level of each sub-point cloud data based on all importance scores includes:

[0096] The first step is to set multiple initial classification results.

[0097] The initial classification results mentioned above include the initial importance level of each sub-point cloud data, which can be the first, second, or third importance level.

[0098] In some embodiments of this application, the initial importance level of each sub-point cloud data can be randomly determined for each initial classification result.

[0099] The second step is to calculate the classification score for each initial classification result based on the importance scores of all sub-point cloud data.

[0100] Specifically, through the formula:

[0101] ;

[0102] ;

[0103] ;

[0104] Calculate the grading score of the initial grading results .

[0105] in, This represents the within-group variance. This represents the variance of the distribution of importance scores at the first importance level. This represents the variance of the distribution of importance scores for the second importance level. This represents the variance of the distribution of importance scores for the third importance level. This represents the variance between groups in the population. For the number of sub-point cloud data, This represents the number of sub-point cloud data points of the first importance level in the initial classification results. This represents the number of sub-point cloud data points of the second importance level in the initial classification results. This represents the number of sub-point cloud data points at the third importance level in the initial classification results. This represents the overall importance mean. This represents the mean importance score of the first importance level in the initial classification results. This represents the mean importance score of the second importance level in the initial grading results. This represents the mean importance score of the third importance level in the initial grading results.

[0106] The third step is to take each initial importance level in the initial classification result with the highest initial score as the importance level of the corresponding sub-point cloud data.

[0107] Step 14: Based on the importance level of each sub-point cloud data, encrypt each sub-point cloud data to obtain the encrypted sub-point cloud data corresponding to each sub-point cloud data.

[0108] In some embodiments of this application, the steps of encrypting each sub-point cloud data based on its importance level to obtain the encrypted sub-point cloud data corresponding to each sub-point cloud data include:

[0109] For each sub-point cloud data, perform the following steps:

[0110] The first step is to determine the encryption equation for the sub-point cloud data based on its importance level.

[0111] The above encryption equation can be a one-dimensional chaotic mapping equation, a two-dimensional chaotic mapping equation, or a three-dimensional chaotic mapping equation.

[0112] When the importance level of the sub-point cloud data is the first importance level, the encryption equation is a one-dimensional chaotic mapping equation.

[0113] When the importance level of the sub-point cloud data is the second most important level, the encryption equation is a two-dimensional chaotic mapping equation.

[0114] When the importance level of the sub-point cloud data is the third importance level, the encryption equation is a three-dimensional chaotic mapping equation.

[0115] It should be noted that the one-dimensional chaotic mapping equation is:

[0116] ;

[0117] in, This represents the system's control parameters, with values ​​ranging from 0 to 4. When 3.5699456 ≤ When the value is ≤4, the system enters a chaotic state, and the state sequence is non-periodic and unpredictable. Indicates the system variable in the th order. The state value after the next iteration Indicates the first The state values ​​after +1 iterations are all between 0 and 1. This represents the number of iterations in the system.

[0118] The two-dimensional chaotic mapping equation is:

[0119] ;

[0120] ;

[0121] in, , These are the system's control parameters. Indicates the first Coordinates after the second iteration The state value, Indicates the first Coordinates after +1 iteration The state value, Indicates the first Coordinates after the second iteration The state value, Indicates the first Coordinates after +1 iteration The state value. When the parameter = 1.4 and When the value is 0.3, the two-dimensional chaotic system will produce a set with a complex fractal structure on the two-dimensional plane, in which the coordinate points will never repeat, but will always be confined to the region of this attractor set.

[0122] The three-dimensional chaotic mapping equation is:

[0123] ;

[0124] ;

[0125] ;

[0126] in, , , These are the system's control parameters. , , Represents system variables, Indicates time or number of iterations. , , Representing system variables respectively , , about The derivative of . When =10, ≥28, When the value is 8 / 3, the system enters a chaotic state.

[0127] The second step is to encrypt the sub-point cloud data using an encryption equation to obtain the encrypted sub-point cloud data corresponding to the sub-point cloud data.

[0128] Specifically, the sub-point cloud data is substituted into the corresponding encryption equation for calculation to obtain the encrypted sub-point cloud data corresponding to the sub-point cloud data.

[0129] For example, the parameters in the encryption equation are initialized as follows: First, the entropy values ​​of the three dimensions are calculated using histogram estimation. The histogram parameter bins=20 controls the number of bins in the histogram. The probability density of each bin is calculated, and then substituted into the Shannon entropy formula. The calculation method is as follows:

[0130] ;

[0131] Selecting the entropy value The highest dimension is used as the encryption dimension. The number of points in the dimension. Let be the coordinates of a point, and normalize its entropy to construct the initial parameters of the one-dimensional chaotic mapping equation, calculated as follows:

[0132] ;

[0133] ;

[0134] Configure system parameters The following one-dimensional chaotic mapping equation is used to generate a chaotic permutation sequence with a length equal to the number of point clouds in the encryption unit, and the permutation encryption operation of the point cloud data is completed. The specific implementation method is as follows:

[0135] ;

[0136] Let the original point cloud in the encryption unit be... Encrypted point cloud Then input , After iterating through the above logistic chaotic system formula... This generates a chaotic sequence. Then, the chaotic sequence is sorted using the following operations to obtain the permutation index. :

[0137] ;

[0138] ;

[0139] in, It is a chaotic sequence The Middle For smaller elements, the replacement operation is as follows:

[0140] ;

[0141] Among them, the code encryption dimension of the original point cloud of dim is the coordinate dimension with the highest entropy obtained above.

[0142] For the two-dimensional chaotic mapping equation, the dimensions with the highest and lowest entropy values ​​of the point cloud data coordinate dimensions in the encryption unit are selected as the encryption dimensions. Based on the entropy values ​​of two dimensions, initial parameters are constructed respectively. With system parameter r=3.9 and iteration count num_iterations=10, the chaotic sequences of the corresponding dimensions can be obtained. , Each chaotic sequence has a length of num_iterations, and the resulting... , Viewed as the initial parameter set of a two-dimensional chaotic mapping equation , This means that num_iterations=10 sets of initial parameters can be constructed ( ),in , Each set of initial parameters ( Both can be iteratively generated using the two-dimensional chaotic mapping equation to produce two sets of chaotic permutation sequences of length h of the point cloud in the encryption unit, and the permutation indices can be obtained respectively. The following operations were performed to obtain a set of encrypted results. :

[0143] ;

[0144] Traversal This will give you the encrypted results for num_iterations groups. .

[0145] Simultaneously, correlation matrices were calculated for both the original and encrypted point cloud data. To further obtain the correlation between dimensions, calculate the correlation between the two times before and after encryption. The calculation formula is: , where (i,j) .

[0146] Encryption results for group num_iterations Both can calculate the correlation between point cloud data before and after encryption. Select the dimensional correlation of the encryption results in the num_iterations group. The largest iteration is used as the encrypted result. The final iteration count, num_iterations, is stored in a JSON file.

[0147] For the three-dimensional chaotic mapping equation, three sets of chaotic sequences are generated ( , , The three coordinate dimensions X, Y, and Z are iteratively permuted multiple times according to a chaotic sequence to enhance chaos and unpredictability, thereby changing the order of points. First, the entropy values ​​of the three dimensions are normalized to construct initial parameters, and after num_iterations iterations, a set of initial parameters for the three-dimensional chaotic mapping equation of length num_iterations is obtained. , , And after processing, a three-dimensional chaotic mapping equation that meets the range is obtained. The calculation is as follows:

[0148] ;

[0149] ;

[0150] ;

[0151] ;

[0152] for( , , ) , , Each iteration generates a chaotic sequence with a length equal to the number of point clouds in the encryption unit, thus yielding a chaotic permutation index sequence. The point cloud data permutation and encryption were completed to obtain a set of encrypted results. For the obtained num_iterations groups of encryption results Selecting the correlation of dimensions before and after encryption The largest iteration is used as the encrypted result. The final iteration count, num_iterations, is stored in a JSON file.

[0153] Step 15: Integrate all encrypted sub-point cloud data to obtain the final encrypted point cloud data of the target 3D point cloud data.

[0154] Specifically, all encrypted sub-point cloud data are integrated into one data according to the corresponding index (generated by the linear octree used in step 11) to obtain the final encrypted point cloud data.

[0155] For example, the octree depth, encryption key, number of iterations num_iterations, and final encrypted point cloud data can be integrated into a single file, and the file can be encrypted using encryption algorithms such as SM4 block cipher algorithm and SM4-ECB electronic codebook mode to obtain the final encrypted file. This encrypted file can be used for tasks such as encrypted transmission of target 3D point cloud data.

[0156] It should be noted that when decrypting encrypted files, the decryption algorithm corresponding to SM4-ECB is used to restore the metadata in the decryption configuration file. The corresponding decryption function and parameters are called according to the encryption level, and point cloud decryption is completed through mask backfilling.

[0157] The method of this application will be illustrated below with a specific example.

[0158] Point cloud data of a certain area, such as Figure 2 As shown, when the octree depth is 1, the final encrypted point cloud data obtained using the method of this application is as follows: Figure 3 As shown, when the octree depth is 2, the final encrypted point cloud data obtained using the method of this application is as follows: Figure 4 As shown, the final decryption result of the encrypted data is as follows: Figure 5 As shown.

[0159] Therefore, it is evident that this application provides high encryption security for point cloud data without destroying the geometric structure, and the error between the decrypted point cloud data and the original point cloud data is small.

[0160] It is worth mentioning that after spatially dividing the 3D point cloud data, the sub-point cloud data is encrypted, and finally integrated to obtain the final encrypted point cloud data. This avoids the geometric structure destruction caused by directly encrypting the entire point cloud, improves the geometric stability of point cloud encryption, and enhances the complexity and strength of point cloud encryption by encrypting according to the importance level of different sub-point cloud data, thereby improving the security of 3D point cloud encryption.

[0161] Furthermore, compared with existing point cloud data encryption technologies, this application overcomes the problems of single encryption strength and lack of spatial adaptive control in existing methods, significantly improving the security, flexibility, and restoration accuracy of point cloud data encryption and decryption. By constructing a three-dimensional index structure based on spatial clustering, the local importance assessment and regional classification of point clouds are effectively realized, laying the foundation for hierarchical encryption. Combining chaotic systems and statistical characteristics such as information entropy, the perturbation dimension and sequence generation are adaptively driven, making the encryption process more robust and highly unpredictable. The proposed encryption and decryption method not only has good reversibility but can also adapt to the different requirements of data protection strength and accuracy for various point cloud processing tasks, possessing high practicality and promotional value. It is suitable for application scenarios such as high-precision three-dimensional model encryption protection, privacy masking of sensitive area point clouds, and hierarchical and controllable point cloud encrypted storage and transmission.

[0162] The following is an exemplary description of the partition encryption device for three-dimensional point cloud data provided in this application.

[0163] like Figure 6As shown, this application embodiment provides a partition encryption device for three-dimensional point cloud data. The partition encryption device 600 for three-dimensional point cloud data includes:

[0164] The partitioning module 601 is used to spatially partition the target 3D point cloud data to obtain multiple sub-point cloud data.

[0165] Calculation module 602 is used to calculate the importance score of each sub-point cloud data for each sub-point cloud data.

[0166] Module 603 is used to determine the importance level of each sub-point cloud data based on all importance scores;

[0167] The encryption module 604 encrypts each sub-point cloud data based on the importance level of each sub-point cloud data to obtain the encrypted sub-point cloud data corresponding to each sub-point cloud data.

[0168] The integration module 605 is used to integrate all encrypted sub-point cloud data to obtain the final encrypted point cloud data of the target 3D point cloud data.

[0169] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0170] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0171] like Figure 7 As shown, an embodiment of this application provides a terminal device, wherein the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 7The diagram shows only one processor, a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 executes the computer program D102 to implement the steps in any of the above method embodiments.

[0172] Specifically, when the processor D100 executes the computer program D102, it spatially divides the target 3D point cloud data to obtain multiple sub-point cloud data. Then, for each sub-point cloud data, it calculates its importance score. Finally, based on all importance scores, it determines the importance level of each sub-point cloud data. Then, based on the importance level of each sub-point cloud data, it encrypts each sub-point cloud data to obtain encrypted sub-point cloud data. Finally, it integrates all encrypted sub-point cloud data to obtain the final encrypted point cloud data of the target 3D point cloud data. In this process, spatially dividing the 3D point cloud data, encrypting the sub-point cloud data, and then integrating them to obtain the final encrypted point cloud data avoids the geometric structure damage caused by directly encrypting the entire point cloud, improving the geometric stability of the point cloud encryption. Encrypting according to the importance level of different sub-point cloud data enhances the complexity and strength of the point cloud encryption, thereby improving the security of the 3D point cloud encryption.

[0173] The processor D100 can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0174] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may be an external storage device of the terminal device D10, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal device D10. Furthermore, the memory D101 may include both internal and external storage units of the terminal device D10. The memory D101 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory D101 can also be used to temporarily store data that has been output or will be output.

[0175] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0176] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0177] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a partition encryption method device / terminal device for three-dimensional point cloud data, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.

[0178] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0179] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0180] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention.

Claims

1. A method for partition encryption of three-dimensional point cloud data, characterized in that, The method comprises the following steps: spatially dividing target three-dimensional point cloud data to obtain a plurality of sub-point cloud data; calculating the importance score of each sub-point cloud data; determining the importance level of each sub-point cloud data according to all importance scores; encrypting each sub-point cloud data based on the importance level of each sub-point cloud data to obtain the corresponding encrypted sub-point cloud data of each sub-point cloud data; integrating all encrypted sub-point cloud data to obtain the final encrypted point cloud data of the target three-dimensional point cloud data; wherein the calculation of the importance score of the sub-point cloud data comprises: through the formula: Computing importance scores for sub-point cloud data ; wherein, represents a spatial cubic volume occupied by the sub-point cloud data, represents a distribution mean square error of point cloud coordinate dimensions in the sub-point cloud data: wherein, denotes a standard deviation of x coordinate distribution of the sub point cloud data, denotes a standard deviation of y coordinate distribution of the sub point cloud data, denotes a standard deviation of z coordinate distribution of the sub point cloud data, denotes a maximum x coordinate of the sub point cloud data, denotes a minimum x coordinate of the sub point cloud data, denotes a maximum y coordinate of the sub point cloud data, denotes a minimum y coordinate of the sub point cloud data, denotes a maximum z coordinate of the sub point cloud data, denotes a minimum z coordinate of the sub point cloud data; determining the importance level of each sub-point cloud data according to all importance scores comprises: setting a plurality of initial classification results; calculating the classification score of the initial classification result based on the importance score of all sub-point cloud data for each initial classification result; each initial importance level in the initial classification result with the highest classification score is taken as the importance level of the corresponding sub-point cloud data.

2. The method of claim 1, wherein, The importance level is a first importance level, a second importance level, or a third importance level. The initial classification result comprises an initial importance level of each sub-point cloud data, and the initial importance level is a first importance level, a second importance level, or a third importance level.

3. The method of claim 2, wherein, The calculation of the classification score of the initial classification result based on the importance score of all sub-point cloud data comprises: through the formula: calculating a ranking score for the initial ranking result ; wherein denotes the overall between-group variance, denotes the distribution variance of the importance scores of the first importance level, denotes the distribution variance of the importance scores of the second importance level, denotes the distribution variance of the importance scores of the third importance level, denotes the overall between-group variance, is the number of sub-point cloud data, is the number of sub-point cloud data of the first importance level in the initial grading result, is the number of sub-point cloud data of the second importance level in the initial grading result, is the number of sub-point cloud data of the third importance level in the initial grading result, denotes the overall importance mean, denotes the importance score mean of the first importance level in the initial grading result, denotes the importance score mean of the second importance level in the initial grading result, denotes the importance score mean of the third importance level in the initial grading result.

4. The method of claim 3, wherein, encrypting each sub-point cloud data based on the importance level of each sub-point cloud data to obtain the corresponding encrypted sub-point cloud data of each sub-point cloud data comprises: the following steps are performed for each sub-point cloud data: determining the encryption equation of the sub-point cloud data according to the importance level of the sub-point cloud data; encrypting the sub-point cloud data using the encryption equation to obtain the corresponding encrypted sub-point cloud data of the sub-point cloud data.

5. The method of claim 4, wherein, The encryption equation is a one-dimensional chaotic mapping equation, a two-dimensional chaotic mapping equation, or a three-dimensional chaotic mapping equation. Determining the encryption equation of the sub-point cloud data according to the importance level of the sub-point cloud data comprises: when the importance level of the sub-point cloud data is a first importance level, the encryption equation is a one-dimensional chaotic mapping equation; when the importance level of the sub-point cloud data is a second importance level, the encryption equation is a two-dimensional chaotic mapping equation; when the importance level of the sub-point cloud data is a third importance level, the encryption equation is a three-dimensional chaotic mapping equation.

6. An apparatus for partition encryption of three-dimensional point cloud data, characterized by comprising: The method comprises the following steps: a division module for spatially dividing target three-dimensional point cloud data to obtain a plurality of sub-point cloud data; a calculation module for calculating the importance score of each sub-point cloud data; a determination module for determining the importance level of each sub-point cloud data according to all importance scores; an encryption module for encrypting each sub-point cloud data based on the importance level of each sub-point cloud data to obtain the corresponding encrypted sub-point cloud data of each sub-point cloud data; An integration module is configured to integrate all the encrypted sub-point cloud data to obtain final encrypted point cloud data of the target three-dimensional point cloud data. The calculation module is specifically configured to implement: According to the formula: Computing importance scores for sub-point cloud data ; wherein, represents a spatial cubic volume occupied by the sub-point cloud data, represents a distribution mean square error of point cloud coordinate dimensions in the sub-point cloud data: wherein, denotes a standard deviation of x coordinate distribution of the sub point cloud data, denotes a standard deviation of y coordinate distribution of the sub point cloud data, denotes a standard deviation of z coordinate distribution of the sub point cloud data, denotes a maximum x coordinate of the sub point cloud data, denotes a minimum x coordinate of the sub point cloud data, denotes a maximum y coordinate of the sub point cloud data, denotes a minimum y coordinate of the sub point cloud data, denotes a maximum z coordinate of the sub point cloud data, denotes a minimum z coordinate of the sub point cloud data; The determination module is specifically configured to implement: Set a plurality of initial classification results; For each initial classification result, calculate a classification score of the initial classification result based on the importance scores of all the sub-point cloud data; Set each initial importance level in the initial classification result with the highest classification score as the importance level of the corresponding sub-point cloud data.

7. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the partition encryption method of the three-dimensional point cloud data according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, the computer-readable storage medium comprising: The computer program is executed by the processor to implement the partition encryption method of the three-dimensional point cloud data according to any one of claims 1 to 5.

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