Method and device for carrying out mosaic management on scattered point cloud data

Abstract

The invention provides a scattered point cloud-based achievement data mosaic management method and device, and the method comprises the steps: extracting first meta-information data from a collected original point cloud file, constructing an initial mosaic point cloud model, and registering the first meta-information data into the initial mosaic point cloud model. And fusing the detail level point cloud files constructed according to the data specification information, and performing three-dimensional grid simplification processing to obtain a global mosaic detail level point cloud data set. And performing data processing optimization on the global mosaic detail level point cloud data set through a data processing optimization process to obtain a final mosaic point cloud data set. According to the method, a mixing technology combining a data set and a database is introduced, mosaic point cloud data set construction is carried out on a plurality of scattered and different types of data, and finally the mosaic point cloud data set is generated, so that the point cloud data set is effectively kept natural, smooth and free of obvious splicing traces or distortion visually, and integrity, accuracy and consistency are ensured.

Description

Technical Field

[0001] This application relates to the technical field of point cloud data processing, and in particular, to a method and device for mosaicking and managing scattered point cloud data, an electronic device, and a storage medium. Background Art

[0002] Point cloud data refers to a set of vectors in a three-dimensional coordinate system, including various information such as position, orientation / angle, distance, time, intensity, etc. The same plane coordinate system of point cloud data can correspond to several different elevation values, which is conducive to expressing detailed information and terrain / features with drastic changes. Point cloud data has measurability, and three-dimensional coordinates, distance, azimuth angle, and surface normal vector can be directly obtained on the point cloud, and the surface area, volume, etc. of the target expressed by the point cloud can also be calculated.

[0003] With the continuous and rapid development of the social economy, airborne, vehicle-mounted, ground and other lidar devices can collect point cloud data with different ranges, different precisions, and different densities, which can be used to generate digital terrain models and provide basic data for topographic mapping, engineering surveying, urban and rural planning, etc.; in addition, point cloud data can be used for special feature extraction through filtering and classification, such as buildings and vegetation. Since point cloud data contains positioning information, it can be used for three-dimensional model reconstruction in digital cities and play an important role in current urban planning, establishment of disaster prevention mechanisms, and overall planning of geographic information systems.

[0004] However, due to the rapid development of the point cloud industry, continuous data accumulation, and continuous expansion of the data range, the resulting massive amount of resultant data has posed a new challenge. Common organizational management methods include management based on file formats and management through database management systems.

[0005] Management based on file formats: Store point cloud data in standard file formats, such as PLY (Polygon File Format) and XYZ (three-dimensional coordinate file format). This method can facilitate reading, writing, and sharing, but for large-scale point cloud data, the reading and writing efficiency may be low.

[0006] Database management system (DBMS) management: Use a database to manage point cloud data, such as relational databases (such as MySQL, PostgreSQL) or databases dedicated to point clouds (such as PGPointCloud, PotreeDB).

[0007] Both of the above two management methods have the following problems: 1. Lack of integrity: Discrete management cannot provide more complete and comprehensive three-dimensional information, including more perspectives and details. 2. Lack of consistency: The coordinate systems may not be unified, and the perspectives may also not be unified. It is impossible to fuse under the same perspective, limited to a single local perspective, and cannot operate and analyze on the entire data set. 3. Lack of accuracy: It is impossible to register deviations and eliminate noise from the data from a global perspective. Summary of the Invention

[0008] The embodiments of the present application provide a method, device, electronic device, and storage medium for mosaicking and managing scattered point cloud data to solve one or more of the above technical problems.

[0009] In a first aspect, an embodiment of the present application provides a method for mosaicking and managing scattered point cloud data, including:

[0010] Extract the first meta-information data contained in each scattered point cloud file from the collected original point cloud files to form a meta-information data set of each scattered point cloud file;

[0011] Construct an initial mosaicked point cloud model based on preset initial meta-information data;

[0012] Register the first meta-information data contained in each scattered point cloud file into the initial mosaicked point cloud model to obtain an initial mosaicked point cloud data set;

[0013] Construct detail-level point cloud files of each scattered point cloud file in the target space reference coordinate system according to the data specification information of the initial mosaicked point cloud data set to form a scattered detail-level point cloud data set;

[0014] Fuse each scattered detail-level point cloud file contained in the scattered detail-level point cloud data set to obtain a global mosaicked point cloud data set;

[0015] Perform three-dimensional mesh simplification processing on the global mosaicked point cloud data set to obtain a global mosaicked detail-level point cloud data set;

[0016] Perform data processing optimization on the global mosaicked detail-level point cloud data set through a data processing optimization process to obtain a final mosaicked point cloud data set.

[0017] In a second aspect, an embodiment of the present application provides a device for mosaicking and managing scattered point cloud data, including:

[0018] Basic information collection module: used to extract the first meta-information data contained in each scattered point cloud file from the collected original point cloud files to form a meta-information data set of each scattered point cloud file;

[0019] Initial Mosaic Point Cloud Model Construction Module: Used to construct an initial mosaic point cloud model based on preset initial meta-information data;

[0020] Registration Module: Used to register the first meta-information data contained in each of the scattered point cloud files into the initial mosaic point cloud model to obtain an initial mosaic point cloud data set;

[0021] Level of Detail Point Cloud File Construction Module: Used to construct level of detail point cloud files of each of the scattered point cloud files in the target space reference coordinate system according to the data specification information of the initial mosaic point cloud data set to form a scattered level of detail point cloud data set;

[0022] Fusion Module: Used to fuse each of the scattered level of detail point cloud files contained in the scattered level of detail point cloud data set to obtain a global mosaic point cloud data set;

[0023] 3D Mesh Simplification Module: Used to perform 3D mesh simplification processing on the global mosaic point cloud data set to obtain a global mosaic level of detail point cloud data set;

[0024] Data Processing Optimization Module: Used to perform data processing optimization on the global mosaic level of detail point cloud data set through a data processing optimization process to obtain a final mosaic point cloud data set.

[0025] In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory. When the processor executes the computer program, the method described in any one of the above is implemented.

[0026] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium. A computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, the method described in any one of the above is implemented.

[0027] Compared with the prior art, the present application has the following advantages:

[0028] According to the embodiments of the present application, first, the first meta-information data included in each scattered point cloud file is extracted from the collected original point cloud files to form a meta-information data set for each scattered point cloud file; and an initial mosaicked point cloud model is constructed based on the preset initial meta-information data; then, the first meta-information data included in each scattered point cloud file is registered into the initial mosaicked point cloud model to obtain an initial mosaicked point cloud data set; then, according to the data specification information of the initial mosaicked point cloud data set, the level-of-detail point cloud files of each scattered point cloud file in the target space reference coordinate system are constructed to form a scattered level-of-detail point cloud data set; after that, the scattered level-of-detail point cloud files included in the scattered level-of-detail point cloud data set are fused to obtain a global mosaicked point cloud data set; finally, the global mosaicked point cloud data set is subjected to three-dimensional mesh simplification processing to obtain a global mosaicked level-of-detail point cloud data set, and the global mosaicked level-of-detail point cloud data set is subjected to data processing optimization through a data processing optimization process to obtain a final mosaicked point cloud data set. By adopting the above solution, a hybrid technology combining a data set and a database can be introduced to construct a mosaicked point cloud data set for multiple scattered and different types of data, and based on the finally generated mosaicked point cloud data set, a data retrieval scheduling mechanism is generated. Since the mosaicked point cloud data set includes the level-of-detail point cloud files of the meta-information data set of each scattered point cloud file constructed by the LOD construction method in the target space reference coordinate system, it can effectively make the point cloud data set visually natural, smooth, without obvious stitching marks or distortion, and there is no significant deviation or deformation in the geometric shape compared with the original point cloud data, retaining the detailed information of the point cloud and ensuring accuracy, such as the edge and shape features of an object, etc. In addition, since the scattered level-of-detail point cloud files included in the scattered level-of-detail point cloud data set are fused, these scattered data are unified into a common space framework to construct a complete and coherent global scene, making the different parts also have consistency, such as color, intensity, etc. And the construction of the mosaicked point cloud data set combines the scattered point cloud data into a whole, providing more comprehensive scene information and ensuring integrity.

[0029] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specific embodiments of the present application are specifically given. Brief Description of the Drawings

[0030] In the drawings, unless otherwise specified, the same reference numerals throughout the several views denote the same or similar components or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments in accordance with the present application and should not be regarded as limiting the scope of the present application.

[0031] Figure 1Schematic diagram of a method for mosaic management of scattered point cloud data provided by this application;

[0032] Figure 2 Flowchart of a method for mosaic management of scattered point cloud data according to an embodiment of this application;

[0033] Figure 3 Block diagram of a device for mosaic management of scattered point cloud data according to an embodiment of this application; and

[0034] Figure 4 Block diagram of an electronic device for implementing the embodiment of this application. Detailed implementation manners

[0035] In the following, only some exemplary embodiments are simply described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the concept or scope of this application. Therefore, the drawings and descriptions are considered to be exemplary in nature and not restrictive.

[0036] To facilitate the understanding of the technical solutions of the embodiments of this application, the related technologies of the embodiments of this application are described below. The following related technologies can be combined with the technical solutions of the embodiments of this application in any way as optional solutions, and they all fall within the protection scope of the embodiments of this application.

[0037] To more clearly illustrate the technical solutions of the embodiments of this specification, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Unless obvious from the language environment or otherwise stated, the same reference numerals in the figures represent the same structure or operation.

[0038] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0039] As shown in this specification and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0040] Flowcharts are used in this specification to illustrate the operations performed by the systems according to the embodiments of this specification. It should be understood that the operations before or after do not necessarily need to be executed precisely in sequence. On the contrary, they can be executed in reverse order or processed simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0041] First, before specifically introducing the technical solutions of the embodiments of the present disclosure, the technical background or the evolution context based on which the embodiments of the present disclosure are made will be introduced first.

[0042] Level of Detail (LOD) point cloud files: That is, LOD (Level of Detail), which is a technology for dynamically adjusting the detail level of an object model according to the importance of the object in the scene (such as factors like the distance from the observer, the size of the object, and the proportion in the picture, etc.). Its working principle is: when the viewpoint is close to the object, a model with a higher level of detail is used, so that the details of the model that can be observed are rich; when the viewpoint is far from the model, a model with a lower level of detail is used to replace the high-detail model. For example, in a 3D scene, distant mountains can be represented by simple geometric shapes (such as a rough outline composed of triangular patches), and when the player approaches the mountains, the system will gradually switch to a more refined model, showing the texture of the mountains, the details of the rocks, etc. The purpose of this is to minimize the amount of graphic data that the computer needs to process and improve the rendering efficiency while ensuring the visual effect.

[0043] Octree Index: It is a data structure technology for spatial data organization and fast retrieval. Its working principle is: it recursively divides the three-dimensional space into eight sub-spaces, forming a tree-like structure. In this process, each node represents a spatial region. If the number of data points contained in this region exceeds a certain threshold or meets specific subdivision conditions, it will continue to be divided into eight sub-regions, and so on. For example, in a large-scale three-dimensional point cloud data scene, if you want to quickly find the point cloud data in a specific region, using the octree index, first start from the root node to determine which sub-space the target region is in, and then continuously search deep along the corresponding branch until the smallest sub-space containing the target data is found. The purpose of this is to be able to efficiently manage and query three-dimensional space data. Compared with the traditional traversal method, it greatly reduces the search time and computational amount, and is widely used in fields such as 3D modeling, computer graphics, and geographic information systems, effectively improving the efficiency and performance of data processing.

[0044] Based on the above background, the applicant provides a method for mosaicking and managing scattered point cloud data through the embodiments of this application to solve all or part of the above technical problems.

[0045] The technical solutions involved in the disclosed embodiments of the present application will be introduced below in combination with the scenarios to which the disclosed embodiments of the present application are applied.

[0046] Figure 1 It is a schematic diagram of an application scenario for exemplarily implementing the method of the embodiments of the present application.

[0047] As Figure 1 shown, in the embodiments of the present application, first, the first meta-information data included in each scattered point cloud file is extracted from the collected original point cloud files, a meta-information data set of each scattered point cloud file is formed based on the extracted first meta-information data, and an initial mosaic point cloud model is constructed based on the preset initial meta-information data; then, the first meta-information data included in each scattered point cloud file is registered into the initial mosaic point cloud model to obtain an initial mosaic point cloud data set; then, according to the data specification information of the initial mosaic point cloud data set, the detail level point cloud files of each scattered point cloud file in the target space reference coordinate system are constructed to form a scattered detail level point cloud data set; after that, the scattered detail level point cloud files included in the scattered detail level point cloud data set are fused to obtain a global mosaic point cloud data set; finally, the global mosaic point cloud data set is subjected to three-dimensional mesh simplification processing to obtain a global mosaic detail level point cloud data set, and the global mosaic detail level point cloud data set is subjected to data processing optimization through a data processing optimization process to obtain a final mosaic point cloud data set.

[0048] The technical solutions of the present application and how the technical solutions of the present application solve the foregoing technical problems will be described in detail below with specific embodiments. The several specific embodiments listed may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0049] As Figure 2 shown, a flowchart of a method for mosaicking and managing scattered point cloud data according to an embodiment of the present application may include:

[0050] In step S201, the first meta-information data included in each scattered point cloud file is extracted from the collected original point cloud files to form a meta-information data set of each scattered point cloud file.

[0051] First, in the embodiments of the present application, the original point cloud file is a collection of three-dimensional space data directly obtained by a three-dimensional scanning device without any processing. It records the coordinates of the object surface in the three-dimensional space in the form of discrete points, and usually also includes additional information (such as color, reflection intensity, normal vector, etc.). The methods for obtaining the original point cloud file by the three-dimensional scanning device include but are not limited to: laser scanning technology, photogrammetry technology, structured light projection technology, etc. It should be noted that the original point cloud file in the present application refers to the general term of a large number of collected point cloud files, rather than a single point cloud of a certain point.

[0052] It should be noted that, in some embodiments of the present application, the meta-information data includes the number of points information, data space range, color information, and intensity information of the point cloud file. And the method for extracting the first meta-information data included in each dispersed point cloud file from the collected original point cloud file specifically includes: determining the coordinate extreme values of the data set corresponding to the original point cloud file based on the functions or methods provided by the point cloud data processing library, and confirming the data space range of the data set corresponding to the original point cloud file based on the coordinate extreme values; statistically analyzing the color information and intensity information of the data set corresponding to the original point cloud file based on the functions or methods provided by the point cloud data processing library; positioning and extracting the number of points information of the data set corresponding to the original point cloud file based on the format header parsing method; obtaining the file size of the data set corresponding to the original point cloud file based on the management mechanism of the original file meta-information data. The collection of the meta-information data constitutes the above-mentioned meta-information data set.

[0053] It should also be noted that the original point cloud file in the embodiments of the present application can come from at least one of the following fields: building three-dimensional reconstruction, virtual reality, autonomous driving environment perception, robot navigation, industrial manufacturing, oil and gas exploration, cultural relic protection, and cultural heritage reconstruction, etc. Further, as long as a large number of discrete point cloud data are collected and used, they can be used as the original point cloud file mentioned in the embodiments of the present application and managed according to the methods in the embodiments of the present application.

[0054] Step S202: Construct an initial mosaic point cloud model based on the preset initial meta-information data.

[0055] In order to uniformly manage the existing large amount of data with standard structures but stored dispersedly, an initial mosaic point cloud model is constructed as a carrier to carry the meta-information data set of each dispersed point cloud file.

[0056] To provide unified standards and context support for subsequent steps, meta-information data is pre-defined and standardized based on scenarios such as system design and business requirements, data collection and preprocessing, and historical experience. Through the preset initial meta-information data, an initial mosaic point cloud model suitable for use in subsequent steps can be constructed. It should be noted that the meta-information data mentioned in this application are all data within the same scope, divided into initial and first only to distinguish whether it is the meta-information data extracted from the original point cloud file. Among them, the meta-information data contained in the scattered point cloud file is defined as the first meta-information data.

[0057] In an alternative embodiment, constructing the initial mosaic point cloud model based on the preset initial meta-information data includes: defining a target spatial reference coordinate system for the initial mosaic point cloud model, and determining the coordinate reference and measurement unit of the initial mosaic point cloud data set in the target spatial reference coordinate system. Among them, the coordinate reference refers to the origin position, axis direction, and reference plane of the target coordinate system (such as geodetic datums like WGS84, CGCS2000, etc.), which is used to establish the absolute spatial positioning framework of the point cloud data, and the measurement unit is used to clarify the coordinate value unit in the coordinate system (such as meters, feet) to ensure the unity of the data quantization standard.

[0058] The target spatial reference coordinate system includes a geographic coordinate system or a projected coordinate system; for each point number in the initial mosaic point cloud model, corresponding color values, reflectivity values, and intensity values are assigned to each point number in the initial mosaic point cloud data set based on the preset color information, reflectivity information, and intensity information.

[0059] Step S203, registering the first meta-information data contained in each scattered point cloud file into the initial mosaic point cloud model to obtain an initial mosaic point cloud data set.

[0060] In an alternative embodiment of the present application, by registering the first meta-information data in the meta-information dataset of the scattered point cloud file into the initial mosaicked point cloud model, the originally scattered data can be managed and stored in a unified model, facilitating subsequent querying, accessing, and processing of the data, and avoiding the problems of management chaos and inconvenient use caused by scattered data. It should be noted that the initial mosaicked point cloud dataset obtained by integrating the first meta-information data contained in each scattered point cloud file has higher usability. Users do not need to search for and integrate information in multiple scattered files and datasets, but can obtain comprehensive data and related attributes in a unified mosaicked point cloud model, greatly improving the data usage efficiency. The registration of meta-information data refers to the process of aligning the meta-information data to a unified coordinate system through geometric transformations (such as rotation and translation), and the registration of meta-information data makes the correlation between point cloud data more explicit, enabling users to better understand the meaning and background of the data. For example, through the acquisition time information in the meta-information data, the time series characteristics of the point cloud data can be understood, and combined with features such as color in the attribute information, the characteristics of the object or scene represented by the point cloud can be analyzed more intuitively.

[0061] Step S204: Construct detailed level point cloud files of each scattered point cloud file in the target space reference coordinate system according to the data specification information of the initial mosaicked point cloud dataset, forming a scattered detailed level point cloud dataset.

[0062] In order to effectively maintain the naturalness and smoothness of the meta-information dataset of each scattered point cloud file visually, without obvious stitching marks or distortion, and without significant deviation or deformation in geometric shape compared to the original point cloud data, while retaining its own detailed information to ensure accuracy, such as the edge and shape features of an object. Therefore, the detailed level point cloud files of the meta-information dataset of each scattered point cloud file are constructed in the target space reference coordinate system through the LOD construction method, thereby forming a scattered detailed level point cloud dataset.

[0063] In an alternative implementation manner, the data specification information of the initial mosaicked point cloud dataset includes the data space range, data accuracy, and target space reference of the initial mosaicked point cloud dataset. Among them, the data space range is usually represented by the minimum and maximum values of three-dimensional coordinates. For example, (X min , X max , Y min , Y max , Z min , Z max)。The data accuracy includes the accuracy of the coordinate positions of each point in the point cloud data and other attributes (such as color, reflectivity, etc.), which is usually measured by the error range. For example, the coordinate accuracy of a point is ±0.01 meters. The target space reference is a reference framework used to define the position, orientation, and scale of the point cloud data in space, including information such as the coordinate system, projection method, elevation datum, etc. Commonly used ones are the 2000 National Geodetic Coordinate System, etc.

[0064] In an alternative embodiment, the method for extracting data specification information of the initial mosaicked point cloud dataset includes methods such as metadata data header parsing, data format and structure reverse inference, and classification label system extraction. Among them, metadata data header parsing can adopt the file header extraction method, that is, directly read key fields such as the coordinate system, elevation datum, and point record format from the header information of the LAS or LAZ format. Step S205, fuse the scattered detail level point cloud files included in the scattered detail level point cloud dataset to obtain a global mosaicked point cloud dataset.

[0065] It should be understood that the scattered detail level point cloud files may each have different space references or coordinate systems. By fusing them in the target space reference coordinate system, these scattered data can be unified into a common space framework to construct a complete and coherent global scene. This can ensure the consistency of all point cloud data in terms of spatial position, facilitating subsequent analysis, processing, and application of the entire scene, and avoiding problems such as data misalignment and overlap caused by inconsistent space references. In addition, the metadata datasets of each scattered point cloud file may have different detail levels. By fusing these point cloud files with different detail levels, the advantages of each dataset can be combined, enabling the global mosaicked point cloud dataset to accurately present an appropriate level of detail in different regions or for different objects. For example, in some areas that need to be focused on, high-detail level data can be used, while in some relatively less important areas, low-detail level data can be used to reduce the data volume, thereby achieving a comprehensive and efficient representation of the entire scene while ensuring data quality.

[0066] In an alternative embodiment, the fusion of the meta - information data sets of multiple scattered point - cloud files into a level - of - detail point - cloud file in the target space reference coordinate system refers to transforming each scattered level - of - detail point - cloud file into a global mosaicked point - cloud data set through geometric alignment, redundancy control, and semantic integration. The specific methods include, but are not limited to, feature - based registration and the Iterative Closest Point (ICP) algorithm. Among them, feature - based registration first extracts feature points or feature regions in the point - cloud data, such as corner points, planes, etc., and then calculates the coordinate transformation parameters by matching these features. The ICP algorithm continuously iterates to find the corresponding points between two point clouds, calculates the optimal translation and rotation parameters, and minimizes the distance error between the two point clouds.

[0067] Step S206: Perform three - dimensional mesh simplification on the global mosaicked point - cloud data set to obtain a global mosaicked level - of - detail point - cloud data set.

[0068] Since three - dimensional point - cloud data usually has a huge amount of data, it occupies a large amount of storage space. Therefore, through three - dimensional mesh simplification, while retaining the main features of the point cloud, the data volume can be significantly reduced, the requirements for storage devices can be lowered, and it is convenient for data storage, transmission, and management. During network transmission or data interaction between different devices, a smaller data volume can greatly improve the transmission speed, reduce the transmission time and bandwidth occupancy, enabling the point - cloud data to be shared and used more quickly between different systems or platforms.

[0069] In an alternative embodiment, three - dimensional mesh simplification refers to performing a scale transformation on the global mosaicked point - cloud data set, scaling the coordinates of each point in the data set according to a preset scaling factor to obtain a global mosaicked point - cloud data set with a changed spatial scale size; using a curvature - based simplification algorithm to gradually reduce the data accuracy of the global mosaicked point - cloud data set with the changed spatial scale size to obtain a global mosaicked point - cloud level - of - detail data set. Three - dimensional mesh simplification is used to reduce data complexity, preserve features while geometrically simplifying, improve data storage and transmission efficiency, reduce the rendering burden on the image processor, and increase the frame rate.

[0070] Step S207: Perform data - processing optimization on the global mosaicked level - of - detail point - cloud data set through a data - processing optimization process to obtain a final mosaicked point - cloud data set.

[0071] First, in the embodiments of the present application, in the face of a huge amount of original point - cloud data (such as city - level LiDAR scans reaching the TB level), direct processing is likely to cause memory overflow and calculation delays. After optimizing the global mosaicked level - of - detail point - cloud data set through a data - processing optimization process, data redundancy and storage pressure can be reduced, multi - resolution rendering efficiency can be improved, and the compatibility of the data - processing process can be enhanced.

[0072] In an alternative embodiment, a method for optimizing the data processing of the global tessellated point cloud level-of-detail data through a data processing optimization process includes: constructing an octree index that traverses the meta-information datasets of the global tessellated level-of-detail point cloud dataset, the scattered level-of-detail point cloud dataset, and the scattered point cloud files; and obtaining the final tessellated point cloud dataset and the scheduling query result of the final tessellated point cloud dataset based on the octree index, the global tessellated point cloud dataset, and the data compression mechanism.

[0073] Further, in an alternative embodiment, after obtaining the scheduling query scheme for the final tessellated point cloud dataset, the method further includes: providing the scheduling query result of the original scattered point cloud file dataset in the original point cloud file based on the scheduling query result of the final tessellated point cloud dataset, in combination with the point cloud rendering process of the graphics processing unit. The scheduling query result of the final tessellated point cloud dataset contains various information about the point cloud data, such as the position and attributes of the points. After receiving this data, the GPU uses it as input for the point cloud rendering process. Usually, a specific graphics API (such as OpenGL, DirectX, etc.) is used to transfer the data from the CPU to the GPU to ensure that the data can be correctly recognized and processed by the GPU. Then, according to the relevant parameters in the scheduling query result, the point cloud rendering parameters of the GPU are configured. For example, according to information such as the density and distribution of the point cloud, appropriate rendering modes (such as point rendering, wireframe rendering, surface rendering, etc.), lighting models, and color mappings are set. If the scheduling query result indicates that the point cloud in certain areas needs to be highlighted, it can be achieved by setting the corresponding rendering parameters.

[0074] By combining the point cloud rendering process of the graphics processing unit, the powerful graphics processing capabilities of the GPU are utilized, enabling the original scattered point cloud file dataset to be presented in a more intuitive and clearer manner. More realistic lighting effects and more delicate texture mappings can be achieved, greatly enhancing the visualization effect of the point cloud data and helping users to more accurately understand and analyze the data. In addition, during the combination process, the original scattered point cloud file dataset can be filtered and screened in real time according to the scheduling query result and the processing capabilities of the GPU. For example, only the point cloud data in the areas of interest to the user is displayed, or the unnecessary points are filtered out according to certain attribute values, thereby reducing the data volume and improving the display efficiency and data processing speed.

[0075] Further, in an optional embodiment, obtaining the final mosaicked point cloud dataset and the scheduling query result of the final mosaicked point cloud dataset based on the octree index, the global mosaicked point cloud dataset, and the data compression mechanism includes: determining the query level using a preset resolution, locking the simplified and precision-matched dataset in the global mosaicked point cloud dataset containing datasets with different precisions, and obtaining the locked dataset; performing spatial filtering on the locked data according to the basic information of each point dataset, and obtaining the dataset with reduced data quantity through the spatial filtering algorithm; performing attribute query on the data with reduced data quantity based on the query attribute requirements, and obtaining the dataset that meets the query attribute requirements.

[0076] To have a more intuitive understanding of the beneficial effects brought by the embodiments of the present application, the following will introduce multiple directions in detailed fields.

[0077] (1) 3D reconstruction and building documentation:

[0078] Registering and fusing the point cloud datasets from different perspectives can generate a high-precision 3D model, which is used in fields such as building documentation, architectural heritage protection, and virtual reality.

[0079] (2) Autonomous driving and robot navigation:

[0080] In autonomous driving and robot navigation, point cloud data is usually used for environmental perception and obstacle detection. Through the mosaicked point cloud algorithm, multiple point cloud datasets can be fused together to provide more comprehensive and accurate environmental perception information, which helps to achieve safer and more intelligent navigation.

[0081] (3) Industrial manufacturing and quality control:

[0082] In industrial manufacturing, point cloud data is usually used to detect and verify the geometric accuracy of products. Through the mosaicked point cloud algorithm, the point cloud datasets from different perspectives can be aligned and fused to more accurately analyze the size, shape, and surface quality of products.

[0083] (4) Geological exploration and underground pipeline detection:

[0084] In oil and gas exploration, mine exploration, and underground pipeline detection, point cloud data can provide 3D information on geology and underground structures. Through the mosaicked point cloud algorithm, multiple point cloud datasets can be fused together to provide more comprehensive and accurate geological and underground structure information, which helps to improve the accuracy and efficiency of geological exploration and pipeline detection.

[0085] (5) Cultural relics protection and cultural heritage reconstruction:

[0086] Point cloud data plays an important role in cultural relic protection and cultural heritage reconstruction. Through the mosaicking point cloud algorithm, the point cloud data sets of cultural relics at different angles can be fused together to restore the complete three-dimensional shape and structure of the cultural relics, which can be used for cultural relic protection, digital museums, and cultural heritage reconstruction.

[0087] It should be understood that the above-introduced actual application fields are based on several specific scenarios to which the mosaicked point cloud data set constructed by the method of the embodiments of the present application is applied. When the embodiments of the present application are applied, they are not limited to the above scenarios and can be applied to any scenario that requires the use of the scattered point cloud results.

[0088] Corresponding to the application scenario and method of the method provided by the embodiments of the present application, the embodiments of the present application also provide a device for mosaicking management of scattered point cloud data. As Figure 3 shown in the structural block diagram of a device for mosaicking management of scattered point cloud data according to an embodiment of the present application, the device for mosaicking management of scattered point cloud data may include:

[0089] Basic information collection module 301: configured to extract the first meta-information data included in each scattered point cloud file from the collected original point cloud files to form a meta-information data set of each scattered point cloud file;

[0090] Initial mosaicked point cloud data set construction module 302: configured to register the first meta-information data included in each of the scattered point cloud files into the initial mosaicked point cloud model to obtain an initial mosaicked point cloud data set;

[0091] Registration module 303: configured to register the first meta-information data included in each of the scattered point cloud files into the initial mosaicked point cloud model to obtain an initial mosaicked point cloud data set;

[0092] Level-of-detail point cloud file construction module 304: configured to construct the level-of-detail point cloud files of each of the scattered point cloud files in the target space reference coordinate system according to the data specification information of the initial mosaicked point cloud data set to form a scattered level-of-detail point cloud data set;

[0093] Fusion module 305: configured to fuse each of the scattered level-of-detail point cloud files included in the scattered level-of-detail point cloud data set to obtain a global mosaicked point cloud data set;

[0094] Three-dimensional mesh simplification module 306: configured to perform three-dimensional mesh simplification processing on the global mosaicked point cloud data set to obtain a global mosaicked level-of-detail point cloud data set;

[0095] Data processing optimization module 307: configured to perform data processing optimization on the global mosaicked level-of-detail point cloud data set through a data processing optimization process to obtain a final mosaicked point cloud data set.

[0096] For the functions of the modules in the devices of the embodiments of the present application, reference may be made to the corresponding descriptions in the above methods, and they have the corresponding beneficial effects, which will not be elaborated here.

[0097] Figure 4 The block diagram of the electronic device for implementing the embodiments of the present application. As Figure 4 shown, the electronic device includes: a memory 410 and a processor 420. A computer program that can run on the processor 420 is stored in the memory 410. When the processor 420 executes the computer program, the method in the above embodiments is implemented. The number of the memory 410 and the processor 420 can be one or more.

[0098] The electronic device further includes:

[0099] A communication interface 430, which is used to communicate with external devices and perform data interaction and transmission.

[0100] If the memory 410, the processor 420, and the communication interface 430 are implemented independently, the memory 410, the processor 420, and the communication interface 430 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 4 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0101] Optionally, in specific implementation, if the memory 410, the processor 420, and the communication interface 430 are integrated on a chip, the memory 410, the processor 420, and the communication interface 430 can communicate with each other through an internal interface.

[0102] The embodiments of the present application provide a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, the method provided in the embodiments of the present application is implemented.

[0103] The embodiments of the present application further provide a chip, which includes a processor, used to call and run an instruction stored in a memory from the memory, so that a communication device installed with the chip executes the method provided in the embodiments of the present application.

[0104] An embodiment of the present application further provides a chip, including: an input interface, an output interface, a processor, and a memory. The input interface, the output interface, the processor, and the memory are connected through an internal connection path. The processor is configured to execute the code in the memory. When the code is executed, the processor is configured to execute the method provided by the embodiment of the application.

[0105] It should be understood that the above-mentioned processor may be a central processing unit (CPU), or may also 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. The general-purpose processor may be a microprocessor or any conventional processor, etc. It is worth noting that the processor may be a processor supporting the advanced reduced instruction set machine (ARM) architecture.

[0106] Further, optionally, the above-mentioned memory may include a read-only memory and a random access memory. The memory may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may include a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may include a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available. For example, static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), sync link dynamic random access memory (SLDRAM), and direct rambus random access memory (DR RAM).

[0107] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0108] In the description of this specification, the descriptions with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0109] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, "a plurality of" means two or more unless otherwise specifically defined.

[0110] Any process or method described in the flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a specific logical function or process. And the scope of the preferred embodiments of this application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed.

[0111] The logic and / or steps described in the flowchart or otherwise described herein, for example, can be considered as a defined sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in connection with these instruction execution systems, apparatus, or devices.

[0112] It should be understood that each part of this application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the method in the above embodiments can be completed by instructing relevant hardware through a program, and this program can be stored in a computer-readable storage medium. When this program is executed, it includes one or a combination of the steps of the method embodiment.

[0113] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a magnetic disk, an optical disc, or the like.

[0114] As described above, only the exemplary embodiments of the present application are provided, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope recorded in the present application can easily think of various changes or substitutions, and these should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for mosaic management of scattered point cloud data, characterized in that, The method includes: Extracting the first meta - information data included in each scattered point cloud file from the collected original point cloud files to form a meta - information data set for each scattered point cloud file; Constructing an initial mosaicked point cloud model based on preset initial meta - information data; Registering the first meta - information data included in each of the scattered point cloud files into the initial mosaicked point cloud model to obtain an initial mosaicked point cloud data set; Constructing detail - level point cloud files of each scattered point cloud file in the target space reference coordinate system according to the data specification information of the initial mosaicked point cloud data set to form a scattered detail - level point cloud data set; Fusing each scattered detail - level point cloud file included in the scattered detail - level point cloud data set to obtain a global mosaicked point cloud data set; Performing three - dimensional mesh simplification processing on the global mosaicked point cloud data set to obtain a global mosaicked detail - level point cloud data set; Performing data processing optimization on the global mosaicked detail - level point cloud data set through a data processing optimization process to obtain a final mosaicked point cloud data set.

2. The method according to claim 1, wherein The meta - information data includes the number of points information, data space range, color information, and intensity information of the point cloud file. The extracting of the first meta - information data included in each scattered point cloud file from the collected original point cloud files includes: Determining the coordinate extreme values of the data set corresponding to the original point cloud file based on functions or methods provided by the point cloud data processing library, and confirming the data space range of the data set corresponding to the original point cloud file based on the coordinate extreme values; Statistically analyzing the color information and intensity information of the data set corresponding to the original point cloud file based on functions or methods provided by the point cloud data processing library; Locating and extracting the number of points information of the data set corresponding to the original point cloud file based on the format header parsing method; Obtaining the file size of the data set corresponding to the original point cloud file based on the management mechanism of the original file meta - information data.

3. The method according to claim 1, wherein The constructing of the initial mosaicked point cloud model based on preset initial meta - information data includes: Defining a target space reference coordinate system for the initial mosaicked point cloud model, determining the coordinate benchmark and measurement unit of the initial mosaicked point cloud model in the target space reference coordinate system, and the target space reference coordinate system includes a geographic coordinate system or a projection coordinate system; For the first meta - information data included in the original point cloud file in the initial mosaicked point cloud model, assigning corresponding color values, reflectivity values, and intensity values to each point number in the initial mosaicked point cloud data set based on preset color information, reflectivity information, and intensity information.

4. The method according to claim 1, wherein The data specification information of the initial mosaicked point cloud data set includes the data space range, data accuracy, and target space reference of the initial mosaicked point cloud data set.

5. The method according to claim 1, characterized in that, The three - dimensional mesh simplification processing process of the global mosaicked point cloud data set includes: Performing a scale transformation on the global mosaicked point cloud data set, performing a scaling operation on the coordinates of each point in the data set according to a preset scaling factor to obtain a global mosaicked point cloud data set with a changed spatial scale size; Adopting a curvature - based simplification algorithm to gradually reduce the data accuracy of the global mosaicked point cloud data set with a changed spatial scale size to obtain a global mosaicked detail - level point cloud data set.

6. The method according to claim 1, wherein The method for optimizing data processing of the global mosaicked level-of-detail point cloud data through a data processing optimization process includes: Constructing an octree index for the meta-information dataset that runs through the global mosaicked level-of-detail point cloud dataset, the decentralized level-of-detail point cloud dataset, and the decentralized point cloud file; Based on the octree index, the global mosaicked point cloud dataset, and a data compression mechanism, obtaining a final mosaicked point cloud dataset and the scheduling query result of the final mosaicked point cloud dataset.

7. The method according to claim 6, characterized in that, After obtaining the scheduling query scheme for the final mosaicked point cloud dataset, the method further includes: Based on the scheduling query result of the final mosaicked point cloud dataset, and in combination with the point cloud rendering process of the graphics processor, providing the scheduling query result of the meta-information dataset of each decentralized point cloud file in the original point cloud file.

8. The method according to claim 7, wherein The obtaining of the final mosaicked point cloud dataset and the scheduling query result of the final mosaicked point cloud dataset based on the octree index, the global mosaicked point cloud dataset, and the data compression mechanism includes: Using a preset resolution to determine the query level, locking the dataset with the refined and precision-matched dataset in the global mosaicked point cloud dataset containing different precision datasets, and obtaining the locked dataset; According to the basic information of each point dataset, performing spatial filtering within the locked data, and through a spatial filtering algorithm, obtaining a dataset with a reduced number of data; Performing an attribute query on the dataset with the reduced number of data based on the query attribute requirements, and obtaining a dataset that meets the query attribute requirements.

9. An apparatus for mosaic management of scattered point cloud data, characterized in that, The device includes: A basic information collection module: used to extract the first meta-information data contained in each decentralized point cloud file from the collected original point cloud files, and form the meta-information dataset of each decentralized point cloud file; An initial mosaicked point cloud model construction module: used to construct an initial mosaicked point cloud model based on preset initial meta-information data; A registration module: used to register the first meta-information data contained in each decentralized point cloud file into the initial mosaicked point cloud model to obtain an initial mosaicked point cloud dataset; A level-of-detail point cloud file construction module: used to construct the level-of-detail point cloud files of each decentralized point cloud file in the target space reference coordinate system according to the data specification information of the initial mosaicked point cloud dataset, and form a decentralized level-of-detail point cloud dataset; A fusion module: used to fuse the decentralized level-of-detail point cloud files contained in the decentralized level-of-detail point cloud dataset to obtain a global mosaicked point cloud dataset; A 3D mesh simplification module: used to perform 3D mesh simplification processing on the global mosaicked point cloud dataset to obtain a global mosaicked level-of-detail point cloud dataset; A data processing optimization module: used to optimize the data processing of the global mosaicked level-of-detail point cloud dataset through a data processing optimization process to obtain a final mosaicked point cloud dataset.

10. An electronic device, including a memory, a processor, and a computer program stored on the memory, where the processor implements the method according to any one of claims 1-8 when executing the computer program.

11. A computer-readable storage medium storing a computer program therein, the computer program, when executed by a processor, implementing the method according to any one of claims 1-8.

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