Point cloud data processing method and device based on B / S architecture and medium

Abstract

The invention discloses a point cloud data processing method and device based on a B / S architecture and a medium, and relates to the technical field of electric digital data processing. The method comprises the following steps: initializing a point cloud data structure corresponding to a point cloud model; functional features corresponding to the processing operation and feature indexes corresponding to the functional features are generated, and the point cloud array is filled with the functional features; according to an operation backspacing demand, determining the position of the specified function feature needing operation backspacing in the point cloud array, and according to the position, executing a forward operation on the operation vernier, so that the forward-moved operation vernier is positioned to a non-backspacing processing operation in the point cloud array; under the condition that the specified functional features need to be subjected to operation recovery, the specified functional features are screened out from the point cloud array according to the flag bits of the functional features in the point cloud array, the size relation between the feature indexes and the operation verniers is determined, operation recovery is conducted on the specified functional features according to the size relation, and the recovered point cloud array is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical digital data processing, and particularly relates to a point cloud data processing method, device and medium based on a B / S architecture. Background Art

[0002] Point cloud data is a collection of three-dimensional spatial data collected by laser scanning or other measurement devices. It is usually represented as a large number of discrete points, each point having a specific coordinate in space, which can accurately capture the geometric shape and surface details of an object, providing a high-precision digital model for the fields of digital shape design, reverse engineering, additive manufacturing, and geological modeling.

[0003] In CAD software, point cloud data can be converted into a three-dimensional model for reverse engineering design. However, due to the large amount of point cloud data, saving a copy of the point cloud data every time a processing operation is performed will bring a large memory pressure to the system; in addition, with a large amount of point cloud data, running point cloud processing algorithms takes a long time, and each undo and redo executes the complete functional process, resulting in an increasing system running time and low processing efficiency. Summary of the Invention

[0004] To solve the above problems, the present invention proposes a point cloud data processing method based on a B / S architecture, including: Receiving a point cloud model imported from the browser side and initializing the point cloud data structure corresponding to the point cloud model; wherein, the point cloud data structure includes a point cloud array, an operation cursor, and a feature index, and each element in the point cloud array is used to mark the processing operation performed on the point cloud data at the current position; For each point cloud data in the point cloud model, positioning the processing operation performed on the point cloud data according to the operation cursor, generating the functional feature corresponding to the processing operation and the feature index corresponding to the functional feature, and filling the functional feature into the point cloud array; wherein, the values corresponding to the operation cursor and the feature index are the same; In the case where the functional feature has a need for operation rollback, determining the position of the specified functional feature that needs to be rolled back in the point cloud array according to the operation rollback requirement, and performing a forward movement operation on the operation cursor according to the position, so that the forward-moved operation cursor positions to the unrolled-back processing operation in the point cloud array; In the case where the specified functional feature needs to be operationally restored, screening out the specified functional feature from the point cloud array according to the flag bits of each functional feature in the point cloud array, determining the size relationship between the feature index and the operation cursor, and performing an operation restoration on the specified functional feature according to the size relationship to obtain the restored point cloud array.

[0005] In an implementation of the present invention, a forward movement operation is performed on the operation cursor according to the position, so that the forward moved operation cursor is positioned at the processing operation that has not been rolled back in the point cloud array. Specifically, it includes: Determine the number of feature bits to be rolled back for the specified functional feature according to the position; Perform a forward movement on the operation cursor by a number corresponding to the number of feature bits, and retain the specified feature index corresponding to the specified functional feature in the feature index; wherein, the operation rollback requirement includes a cancellation requirement and a rollback requirement.

[0006] In an implementation of the present invention, the types of operation recovery include reconstruction and cancellation of rollback. According to the size relationship, an operation recovery is performed on the specified functional feature to obtain the restored point cloud array. Specifically, it includes: Determine the operation rollback type corresponding to the specified functional feature; wherein, the operation rollback type includes cancellation and rollback; According to the size relationship, determine whether there is a new functional feature in the point cloud array after the operation rollback is completed, so as to determine the operation recovery mode corresponding to the specified functional feature; wherein, the operation recovery mode is an index recovery mode and a synchronization recovery mode; Based on different operation recovery modes, perform an operation recovery on the specified functional feature to obtain the restored point cloud array.

[0007] In an implementation of the present invention, according to the size relationship, determine whether there is a new functional feature in the point cloud array after the operation rollback is completed, so as to determine the operation recovery mode corresponding to the specified functional feature. Specifically, it includes: When the feature index is not greater than the operation cursor, it is determined that there is a new functional feature in the point cloud array after the operation rollback is completed, and the operation recovery mode corresponding to the specified functional feature is the synchronization recovery mode; When the feature index is greater than the operation cursor, it is determined that there is no new functional feature in the point cloud array after the operation rollback is completed, and the operation recovery mode corresponding to the specified functional feature is the index recovery mode.

[0008] In an implementation of the present invention, based on different operation recovery modes, perform an operation recovery on the specified functional feature to obtain the restored point cloud array. Specifically, it includes: Based on the above index recovery mode, when the type of the operation recovery is reconstruction, the operation cursor is successively moved backward to the end position of the point cloud array to obtain the recovered point cloud array. Or, when the type of the operation recovery is rollback cancellation, the rollback operations on the specified functional features are successively cancelled to obtain the corresponding point cloud array. Based on the above synchronous recovery mode, when the type of the operation recovery is reconstruction, the reconstruction operation on the specified functional feature is prohibited. Or, when the type of the operation recovery is rollback cancellation, the specified functional features created before the rollback are successively reconstructed. After reconstructing any specified functional feature, the specified feature index corresponding to the specified functional feature is updated so that the updated specified feature index is consistent with the operation cursor until the rollback operations on all specified functional features are cancelled.

[0009] In an implementation manner of the present invention, successively reconstructing the specified functional features created before the rollback, and after reconstructing any specified functional feature, updating the specified feature index corresponding to the specified functional feature so that the updated specified feature index is consistent with the operation cursor specifically includes: Positioning the operation cursor to the position where the last feature in the new functional features is located; According to the sequence of the specified functional features, successively reconstructing the specified functional features created before the rollback. After completing the reconstruction of any specified functional feature, moving the operation cursor backward by one position, and adjusting the specified feature index corresponding to the specified functional feature to be consistent with the operation cursor.

[0010] In an implementation manner of the present invention, screening out the specified functional features from the point cloud array according to the flag bits of each functional feature in the point cloud array specifically includes: Obtaining the flag bits of each functional feature in the point cloud array; Screening out the functional features with the flag bits being the preset value from the point cloud array as the specified functional features; wherein, the preset value and the flag bits corresponding to the other functional features except the specified functional features in the functional features are in a binary state distribution.

[0011] In an implementation manner of the present invention, the processing operation at least includes any one or more of the following: point cloud simplification, point cloud positioning, point cloud denoising, removing redundant points, and point cloud meshing.

[0012] An embodiment of the present invention provides a point cloud data processing device based on a B / S architecture. The device includes: At least one processor; And a memory communicatively connected to the at least one processor; Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute a point cloud data processing method based on the B / S architecture as described in any one of the above.

[0013] An embodiment of the present invention provides a non-volatile computer storage medium storing computer-executable instructions, and the computer-executable instructions are set as: A point cloud data processing method based on the B / S architecture as described in any one of the above.

[0014] The point cloud data processing method based on the B / S architecture proposed by the present invention can bring the following beneficial effects: All processing operations corresponding to the point cloud data are recorded through a point cloud array. Only one copy of the point cloud data needs to be maintained, reducing the system memory occupancy and improving the system resource utilization efficiency. At the same time, by directly marking the currently effective operation position through an operation cursor and maintaining the mapping relationship between features and point cloud data through a feature index, rapid positioning between different functional features can be achieved, and processes such as undo, redo, and rollback between different functional features can be quickly performed without repeatedly executing the point cloud processing algorithm for each operation, reducing the running time of the CAD system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings: Figure 1 It is a schematic flowchart of a point cloud data processing method based on the B / S architecture provided by an embodiment of the present invention; Figure 2 It is a schematic flowchart of creating a functional feature provided by an embodiment of the present invention; Figure 3 It is another schematic flowchart of creating a functional feature provided by an embodiment of the present invention; Figure 4 It is a schematic diagram of the data structure before an undo operation provided by an embodiment of the present invention; Figure 5 It is a schematic diagram of the data structure after an undo operation provided by an embodiment of the present invention; Figure 6 It is a schematic flowchart of undo / redo when no new feature is added provided by an embodiment of the present invention; Figure 7 It is a schematic diagram of the data structure after rollback provided by an embodiment of the present invention; Figure 8Schematic diagram of the data structure after canceling the rollback operation in the first scenario provided by the embodiment of the present invention; Figure 9 Schematic diagram of the data structure after canceling the rollback operation in the second scenario provided by the embodiment of the present invention; Figure 10 Schematic diagram of the data structure after canceling the rollback operation in the third scenario provided by the embodiment of the present invention; Figure 11 Schematic diagram of the structure of a point cloud data processing device based on the B / S architecture provided by the embodiment of the present invention. Detailed implementation manners

[0016] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] The embodiment of the present invention provides a CAD system, which adds point cloud simplification, point cloud positioning, point cloud denoising, redundant point removal, point cloud meshing and other processing functions on the basis of the traditional basic modeling function. Based on the data generated by these point cloud processing operations, different point cloud functions can be maintained and processed, so as to provide high-precision digital models for the fields of digital shape design, reverse engineering, additive manufacturing and geological modeling. However, the traditional point cloud data processing method needs to save a copy of point cloud data every time a point cloud processing operation is executed, which will bring great memory pressure to the system. Moreover, point cloud processing is a computationally intensive and memory-intensive digital shape design method, and the traditional point cloud processing software deployed on a local machine based on the C / S (Client-Server) architecture limits the data processing efficiency and scalability. While the point cloud processing software based on the B / S (Broswer-Server) architecture can make full use of the computing power of the server and adapt to the large-scale data processing requirements. Therefore, the embodiment of the present invention is based on the B / S architecture to realize the processes of cancellation, redo and rollback between different features, without repeatedly executing the point cloud processing algorithm for each operation, and reducing the running time of the CAD system.

[0018] The following will describe in detail the technical solutions provided by each embodiment of the present invention with reference to the drawings.

[0019] As Figure 1 shown, a point cloud data processing method based on the B / S architecture provided by the embodiment of the present invention includes: S101: Receive the point cloud model imported by the browser side, and initialize the point cloud data structure corresponding to the point cloud model; wherein, the point cloud data structure includes a point cloud array, an operation cursor, and a feature index, and each element in the point cloud array is used to mark the processing operation performed on the point cloud data at the current position.

[0020] Based on the B / S architecture, the server receives the point cloud model imported by the browser side and initializes the point cloud data structure corresponding to the point cloud model. The point cloud model contains a large number of discrete points representing three-dimensional objects or scenes. By converting the point cloud data into a three-dimensional model, reverse engineering design can be carried out. Each point cloud data in the point cloud model maintains a 128-bit point cloud data structure, and the point cloud data structure includes a point cloud array, an operation cursor, and a feature index. Each element in the point cloud array is used to mark the processing operation performed on the point cloud data at the current position, and the processing operations include at least any one or more of the following: point cloud simplification, point cloud positioning, point cloud denoising, removing redundant points, and point cloud meshing. The operation cursor is used to mark the current operation position, indicating which step of the processing operation is currently being carried out, and the feature index is used to represent which feature is the currently created feature, which is convenient for updating the data in the data structure when creating, undoing, redoing, and rolling back features.

[0021] Therefore, based on the traditional point cloud data structure, the present invention records all the processing operations corresponding to the point cloud data through the point cloud array, only needs to maintain one copy of the point cloud data, reduces the system memory occupancy, and improves the system resource utilization efficiency. At the same time, by directly marking the currently effective operation position through the operation cursor and maintaining the mapping relationship between the feature and the point cloud data through the feature index, fast positioning between different functional features can be achieved.

[0022] S102: For each point cloud data in the point cloud model, locate the processing operation performed on the point cloud data according to the operation cursor, generate the functional feature corresponding to the processing operation and the feature index corresponding to the functional feature, and fill the functional feature into the point cloud array; wherein, the values of the operation cursor and the feature index are the same.

[0023] After the point cloud data structure is initialized, it does not have actual feature values, and data filling for the point cloud data structure is still required based on the processing operations on the point cloud data in the point cloud model. This process is essentially a process of creating functional features. After importing the point cloud model, locate the processing operation performed on the point cloud data according to the operation cursor. At this time, the operation cursor cur = 1. Then, execute the corresponding processing operation to complete the creation of the functional feature. At the same time, generate the feature index corresponding to the functional feature. At this time, the values of the operation cursor and the feature index are the same, that is, index = cur = 1. After the creation of the functional feature is completed, the functional feature needs to be filled into the corresponding position in the point cloud array, and the functional feature can be located according to the operation cursor cur = 1. AsFigure 2 As shown in the schematic flowchart of creating functional features, the point cloud array HiddenFlag is used to store the processing operations performed on the point cloud data. When creating the first functional feature in the point cloud array, the operation cursor cur = index = 1, and the first functional feature can be located according to the operation cursor. As Figure 3 shown, when creating the second functional feature, the operation cursor moves backward. At this time, cur = 2, which is used to indicate the second position in HiddenFlag. After the creation of the functional feature is completed, the data at the position where cur = 2 in the point cloud array will be updated, and the second functional feature will be filled in this position. At this time, the feature index (i.e., feature index) is consistent with the operation cursor, and index = cur = 2.

[0024] S103: In the case where there is a need for operation rollback for functional features, according to the operation rollback requirement, determine the position of the specified functional feature that needs to be rolled back in the point cloud array, and perform a forward movement operation on the operation cursor according to the position, so that the forward-moved operation cursor locates the unrolled-back processing operation in the point cloud array.

[0025] Traditional point cloud processing algorithms save a copy of the point cloud data every time a processing operation is performed, which not only brings a large memory pressure to the system, but also when performing feature cancellation, redo, or rollback, since the operation logic is not clear, it is necessary to start from the initial state and execute the complete functional process in sequence until rolling back to the target state. This global operation mode will significantly increase the running time of the system.

[0026] Based on this, in the case where there is an operation rollback requirement such as a cancellation requirement and a rollback requirement for functional features in the embodiments of the present invention, it is no longer necessary to re-execute all the processing operations performed on the point cloud data completely, but according to the operation rollback requirement, determine the position of the specified functional feature that needs to be rolled back in the point cloud array. In this way, a forward movement operation can be performed on the operation cursor according to this position, so that the forward-moved operation cursor locates the unrolled-back processing operation in the point cloud array.

[0027] Specifically, after the system locates the position of the specified functional feature that requires operation rollback, it can determine the number of feature bits to be rolled back for the specified functional feature based on this position and the difference before the current operation cursor in the point cloud array. Then, the operation cursor is moved forward by a number corresponding to the number of feature bits. Each time it is moved forward one step, the functional feature pointed to by the operation cursor is revoked or rolled back. If the current marker bit value at a certain position is 1, it means that the point has been rolled back in this operation; if the current marker bit value is 0, it means that the point has been retained in this operation. In this way, by operating on the operation cursor, the system can directly roll back to the previous processing operation according to the data recorded in the point cloud array, instead of, like traditional point cloud processing methods, re-executing the entire implementation process of the previous operation from the starting position. It can complete the revocation process in a short time and reduce the system operation pressure. It should be noted that during the operation rollback process, the system only performs corresponding operations on the operation cursor, and the specified feature index corresponding to the specified functional feature in the feature index will be completely retained to provide feature position indication for subsequent operation restoration.

[0028] Figure 4 and Figure 5 are schematic diagrams of the data structures before and after the revocation operation, respectively. As Figure 4 shown, assume that 6 features have been created currently, the operation cursor cur-index = 6, and the functional features corresponding to six processing operations are also saved in the point cloud array. If the current user wants to revoke the 6th processing operation, only need to move the operation cursor cur from 6 to 5. During the forward movement of the operation cursor, the system will automatically revoke the functional feature at the position where the operation cursor is 6. At this time, cur = 5 and index = 6.

[0029] S104: In the case where the specified functional feature needs to be operationally restored, filter out the specified functional feature from the point cloud array according to the marker bits of each functional feature in the point cloud array, determine the size relationship between the feature index and the operation cursor, and perform operation restoration on the specified functional feature according to the size relationship to obtain the restored point cloud array.

[0030] After the specified functional feature is revoked or rolled back through the above process, if you want to perform undo-redo or cancel the rollback, you need to filter out the specified functional feature according to the flag bits of each functional feature in the point cloud array. According to the previous operation rollback process, for the functional feature that has undergone operation rollback, its corresponding flag bit is 1. Based on this, obtain the flag price of each functional feature in the point cloud array, and filter out the functional feature with the flag bit being the preset value from the point cloud array as the specified functional feature. Among them, the preset value is 1, and the preset value and the flag bits corresponding to other functional features except the specified functional feature in the functional feature are in a binary state distribution, and the flag bits corresponding to other functional features are 0. After filtering out the specified functional feature, according to the size relationship between the feature index and the operation cursor, determine whether new features are added after the operation rollback, so as to perform operation recovery on the specified functional feature and obtain the restored point cloud array.

[0031] In one embodiment, for the specified functional feature to be operationally restored, first determine whether the type of operation rollback it has undergone is revocation or rollback. In this way, when performing operation recovery later, different recovery operations can be executed based on different types of operation rollbacks. Then, according to the size relationship between the feature index and the operation cursor, determine whether there are new functional features in the point cloud array after the operation rollback is completed, so as to determine the operation recovery mode corresponding to the specified functional feature.

[0032] Specifically, the operation recovery mode includes an index recovery mode and a synchronous recovery mode. When the feature index is not greater than the operation cursor, it indicates that there are new functional features in the point cloud array after revocation. At this time, the feature index has not been synchronously updated, and the operation cursor points from the position before the revoked feature to the position of the current new functional feature. In this case, the operation recovery mode corresponding to the specified functional feature is the synchronous recovery mode. The synchronous recovery mode refers to the process of data recovery according to the synchronous relationship between the operation cursor and the feature index. In this mode, it needs to be completed synchronously through the feature index and the operation cursor. When the feature index is greater than the operation cursor, it indicates that no new features are added after revocation. At this time, the operation recovery mode corresponding to the specified functional feature is the index recovery mode. In this mode, if you want to perform an operation rollback on the revoked or rolled-back feature, you only need to perform a guided recovery of the functional feature according to the feature index.

[0033] In one embodiment, in the index recovery mode, no new functional features are added. If you want to perform a revocation and reconstruction of a feature, because the data of each previous operation is saved in HiddenFlag and is not deleted or modified during revocation, at this time, according to the feature index, you can directly redo the feature. For example Figure 6The following is a schematic diagram of the undo and redo process without adding new features. When performing a feature redo after an undo, cur = 5, and the feature index to be redone is index = 6. Compare the size relationship between the operation cursor and the feature index. Since the feature index is greater than the operation cursor, it is determined that it is in the index recovery mode at this time. If a redo is to be performed after a feature undo, the feature data corresponding to each previous processing operation is still saved in HiddenFlag at this time, and it is not deleted or modified when the feature is undone. Therefore, only according to the value of the feature index, directly move the operation cursor from the current position to be consistent with the feature index, that is, move cur one position backward to 6 to complete the feature redo. If the type of operation recovery is cancel rollback, cancel the rollback operation of the specified functional feature in sequence until the operation cursor and the feature index are consistent, and the restored point cloud array is obtained.

[0034] In the synchronous recovery mode, if there are new functional features in the point cloud array after a feature is undone or rolled back, the newly added feature data will overwrite the data in the original point cloud array. After the data is overwritten, if you want to perform an undo and reconstruction again, the system will not support such an operation, and will prohibit the reconstruction operation of the specified functional feature. At the same time, the operation cursor will not be moved either. When operating on the operation cursor later, only new functional features will be created on the basis of the current data.

[0035] For the case where the operation recovery type is cancel rollback, its difference from undo and reconstruction is that after a rollback, new functional features are created, and then cancel rollback is selected. The features that were rolled back before will be reconstructed based on the newly added functional features, and the index value of the feature index and the data in HiddenFlag will also be updated. Based on this, when performing an operation recovery on the rollback data, it is necessary to reconstruct the specified functional features created before the rollback in sequence, and after reconstructing any specified functional feature, update the specified feature index corresponding to the specified functional feature so that the updated specified feature index is consistent with the operation cursor until the rollback operation of all specified functional features is cancelled.

[0036] Specifically, first, position the operation cursor at the position of the last feature in the new functional features, and then, in the order of the specified functional features, reconstruct the specified functional features created before the rollback in sequence. After completing the reconstruction of any specified functional feature, move the operation cursor one position backward and adjust the specified feature index corresponding to the specified functional feature to be consistent with the operation cursor. Repeat this process continuously until the rollback operation of all specified features is cancelled.

[0037] Such as Figure 7Schematic diagram of a data structure after rollback. Suppose the user rolls back to the third step, i.e., cur = 3, and the user adds two new functional features after the current feature. At this time, cur = 5, and the original data of cur = 4 and cur = 5 in HiddenFlag has been replaced with the data of the newly added features. At this time, the feature index includes not only the indexes corresponding to the newly added functional features 4 and 5, but also the indexes corresponding to the rollback features 4, 5, and 6. If the operation of canceling the rollback needs to be performed after adding the features, it is necessary to reconstruct the three features 4 - 6 created before clicking the rollback. The specific process is as follows: Canceling the rollback of feature 4: As Figure 7 shown, at this time, the feature index index = 4, while cur = 5, index <= cur, indicating that new functional features have been created before rolling back the current feature. Therefore, create new feature data cur++, generate a new feature, and update the index of the current feature to cur = 6. At the same time, save the data of the feature to the cur = 6 field in HiddenFlag. After performing the above operations, the data in the data structure is as Figure 8 shown. At this time, the operation of canceling the rollback of rollback feature 4 has been completed, and its corresponding functional feature is located at the position of cur = 6.

[0038] Canceling the rollback of feature 5: At this time, the index of the feature is 5, while cur = 6, index <= cur, indicating that new functional features have been created before rolling back the feature. Therefore, create new feature data cur++, generate a new functional feature, and update the index of the current feature to cur = 7. At the same time, save the data of the feature to the cur = 7 field in HiddenFlag. After performing the above operations, the data in the data structure is as Figure 9 shown. At this time, the operation of canceling the rollback of rollback feature 5 has been completed, and its corresponding functional feature is located at the position of cur = 7.

[0039] Canceling the rollback of feature 6: At this time, the index of the feature is 6, while cur = 7, index <= cur, indicating that new functional features have been created before rolling back the feature. Therefore, create new feature data cur++, generate a new functional feature, and update the index of the current feature to cur = 8. At the same time, save the data of the feature to the cur = 8 field in HiddenFlag. After performing the above operations, the data in the data structure is as Figure 10 shown. At this time, the operation of canceling the rollback of rollback feature 6 has been completed, and its corresponding functional feature is located at the position of cur = 8.

[0040] The above are the method embodiments proposed by the present invention. Based on the same concept, some embodiments of the present invention also provide the corresponding devices and non-volatile computer storage media for the above methods.

[0041] Figure 11 It is a schematic structural diagram of a point cloud data processing device based on the B / S architecture provided by the embodiments of the present invention. As Figure 11 shown, it includes: At least one processor; and, A memory communicatively connected to at least one processor; wherein, The memory stores instructions executable by at least one processor, and the instructions are executed by at least one processor so that at least one processor can execute a method for processing point cloud data based on the B / S architecture as described in any one of the above.

[0042] The embodiments of the present invention provide a non-volatile computer storage medium storing computer-executable instructions, and the computer-executable instructions are set as: A method for processing point cloud data based on the B / S architecture as described in any one of the above.

[0043] The various embodiments in the present invention are all described in a progressive manner. For the same or similar parts among the various embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the partial description of the method embodiments.

[0044] The devices and media provided by the embodiments of the present invention correspond one-to-one with the methods. Therefore, the devices and media also have beneficial technical effects similar to those of the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be elaborated here.

[0045] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0046] The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block of the flowchart illustrations and / or block diagrams, and combinations of flows and / or blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processors of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing device create means for implementing the functions specified in the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0047] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0048] These computer program instructions may also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0049] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0050] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media.

[0051] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0052] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but also other elements not expressly listed, or elements that are inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0053] The above are only embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. A point cloud data processing method based on B / S architecture, characterized in that: The method comprises: Receive the point cloud model imported by the browser, and initialize the point cloud data structure corresponding to the point cloud model; wherein the point cloud data structure includes a point cloud array, an operation cursor and a feature index, and each element in the point cloud array is used to mark the processing operation performed on the point cloud data at the current position; For each point cloud data in the point cloud model, the processing operation performed on the point cloud data is located according to the operation cursor, and for the located processing operation, a functional feature corresponding to the processing operation and a feature index corresponding to the functional feature are generated, and the functional feature is filled into the point cloud array; wherein the numerical values ​​corresponding to the operation cursor and the feature index are the same; In the case where the functional feature has an operation rollback requirement, determining the position of the designated functional feature that needs to be operated rollback in the point cloud array according to the operation rollback requirement, and performing a forward movement operation on the operation cursor according to the position, so that the operation cursor after the forward movement is positioned at the processing operation that has not been rolled back in the point cloud array; In the case where the designated functional feature needs to be restored, the designated functional feature is screened out from the point cloud array according to the mark bits of each functional feature in the point cloud array, the size relationship between the feature index and the operation cursor is determined, and based on the size relationship, the designated functional feature is restored to obtain the restored point cloud array.

2. The point cloud data processing method based on B / S architecture according to claim 1, characterized in that: Performing a forward operation on the operation cursor according to the position so that the forward-moved operation cursor is positioned at a processing operation that has not been rolled back in the point cloud array, specifically includes: Determine, according to the position, the number of feature bits required to be rolled back for the specified functional feature; The operation cursor is moved forward by an amount corresponding to the number of feature bits, and the specified feature index corresponding to the specified functional feature in the feature index is retained; wherein the operation rollback requirement includes a cancellation requirement and a rollback requirement.

3. The point cloud data processing method based on B / S architecture according to claim 1, characterized in that: The types of operation recovery include reconstruction and cancel rollback. According to the size relationship, the operation recovery is performed on the specified functional feature to obtain the restored point cloud array, which specifically includes: Determine the operation rollback type corresponding to the specified functional feature; wherein the operation rollback type includes undo and rollback; According to the size relationship, determining whether the point cloud array has a newly added functional feature after the operation rollback is completed, so as to determine the operation recovery mode corresponding to the specified functional feature; wherein the operation recovery mode is an index recovery mode and a synchronous recovery mode; Based on different operation recovery modes, the designated functional features are operated and recovered to obtain the restored point cloud array.

4. The point cloud data processing method based on B / S architecture according to claim 3 is characterized in that: According to the size relationship, determining whether the point cloud array has a newly added functional feature after the operation rollback is completed, so as to determine the operation recovery mode corresponding to the specified functional feature, specifically includes: When the feature index is not greater than the operation cursor, it is determined that the point cloud array has a newly added functional feature after the operation rollback is completed, and the operation recovery mode corresponding to the specified functional feature is the synchronous recovery mode; In the case where the feature index is greater than the operation cursor, it is determined that the point cloud array does not have the newly added functional feature after the operation rollback is completed, and the operation recovery mode corresponding to the specified functional feature is the index recovery mode.

5. The point cloud data processing method based on B / S architecture according to claim 4 is characterized in that: Based on different operation recovery modes, the specified functional features are operated and restored to obtain the restored point cloud array, specifically including: Based on the index recovery mode, when the type of the operation recovery is reconstruction, the operation cursor is moved back to the end position of the point cloud array in sequence to obtain the restored point cloud array, or, when the type of the operation recovery is cancel rollback, the rollback operation of the specified functional features is canceled in sequence to obtain the corresponding point cloud array; Based on the synchronous recovery mode, when the type of operation recovery is reconstruction, the reconstruction operation of the specified functional features is prohibited, or, when the type of operation recovery is to cancel the rollback, the specified functional features created before the rollback are rebuilt in sequence, and after rebuilding any specified functional feature, the specified feature index corresponding to the specified functional feature is updated to make the updated specified feature index consistent with the operation cursor until the rollback operation of all specified functional features is canceled.

6. The point cloud data processing method based on B / S architecture according to claim 5, characterized in that: The designated functional features created before the rollback are rebuilt in sequence, and after any designated functional feature is rebuilt, the designated feature index corresponding to the designated functional feature is updated so that the updated designated feature index is consistent with the operation cursor, specifically including: Positioning the operation cursor at the location of the last feature in the newly added functional features; The specified functional features created before the rollback are rebuilt in sequence according to the sequence of the specified functional features. After the reconstruction of any specified functional feature is completed, the operation cursor is moved back one position, and the specified feature index corresponding to the specified functional feature is adjusted to be consistent with the operation cursor.

7. The point cloud data processing method based on B / S architecture according to claim 1, characterized in that: Filtering the designated functional features from the point cloud array according to the mark bits of each functional feature in the point cloud array specifically includes: Obtaining the marking position of each functional feature in the point cloud array; The functional feature whose mark bit is a preset value is selected from the point cloud array as the designated functional feature; wherein the preset value and the mark bits corresponding to other functional features among the functional features except the designated functional feature are distributed in a binary state.

8. The point cloud data processing method based on B / S architecture according to claim 1, characterized in that: The processing operation includes at least one or more of the following: point cloud simplification, point cloud positioning, point cloud denoising, redundant point removal, and point cloud meshing.

9. A point cloud data processing device based on B / S architecture, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; In which, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a point cloud data processing method based on a B / S architecture as described in any one of claims 1-8.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are configured to: A point cloud data processing method based on a B / S architecture as described in any one of claims 1 to 8.

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