Three-dimensional scanning method and device for automatically identifying detail area, equipment and medium

Abstract

The invention provides a three-dimensional scanning method for automatically identifying detail areas, which can be applied to the technical field of three-dimensional scanning and modeling. The method comprises the following steps: executing a first scanning process, and obtaining an initial three-dimensional grid model of a target object; on the basis of the initial three-dimensional grid model, automatic identification processing of a detail region is executed, and a detail feature region on the target object is identified; executing a second scanning process, and performing high-resolution scanning on the identified detail feature region to obtain three-dimensional data of the detail region; and performing fusion processing on the initial three-dimensional grid model and the three-dimensional data of the detail area to generate a final three-dimensional model.

Description

Technical Field

[0001] This invention relates to the field of 3D scanning and modeling technology, and more specifically to a 3D scanning method, apparatus, device, and medium for automatically identifying detailed regions. Background Technology

[0002] In the field of 3D scanning and modeling technology, obtaining high-precision and high-completeness 3D digital models is the core technical goal. Currently, mainstream 3D scanning methods typically require operators to manually set the global voxel resolution (density) before scanning, and the final output digital model is mostly expressed in the form of an evenly spaced grid. In actual operation, if it is necessary to perform fine-scale modeling of specific detailed areas on the surface of an object (such as edges, holes, depressions, etc.), the operator must manually observe and select these areas of interest through host computer software after completing the first scan, and then start a second or even multiple scanning processes.

[0003] This existing technical solution, which relies on manual judgment and selection, has obvious drawbacks: First, for features such as complex shapes, intricate structures, irregular edges, curved areas, or holes without sidewalls that are outside the optimal field of view, operators find it difficult to quickly and accurately complete manual selection, leading to incomplete detail capture or modeling failure. Second, the entire scanning process is inefficient due to frequent manual intervention, which not only increases operational complexity and time costs but also places high demands on the professional experience of operators. In addition, the subjectivity of manual selection may lead to inconsistent results between different operators or different scanning tasks, affecting the reliability and repeatability of 3D data.

[0004] Therefore, existing technologies lack a method that can automatically and intelligently identify detailed areas on the surface of an object and guide the scanning system to perform multi-resolution adaptive acquisition. This has become a key bottleneck restricting the development of 3D scanning technology towards high efficiency, automation, and intelligence. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address at least one of the aforementioned problems, this invention provides a 3D scanning method, apparatus, device, and medium for automatically identifying detailed regions. It employs an automatic identification algorithm and a multi-resolution scanning fusion method, identifying edge features through normal vector angle calculation and detecting structures without sidewall holes based on edge proportion analysis, thus solving the scanning challenges of curved areas and blind spots. The system achieves detailed modeling through multi-resolution scanning and mesh stitching, and utilizes neighborhood optimization to ensure boundary accuracy. This solution is applicable to fields such as industrial inspection and cultural relic digitization, providing technical support for intelligent manufacturing and digital twins.

[0007] (II) Technical Solution

[0008] To address the aforementioned technical problems, embodiments of the present invention provide a three-dimensional scanning method, apparatus, device, and medium for automatically identifying detailed regions.

[0009] According to a first aspect of the present invention, a three-dimensional scanning method for automatically identifying detailed regions is provided, comprising: performing a first scanning process to obtain an initial three-dimensional mesh model of a target object; performing automatic detailed region identification processing based on the initial three-dimensional mesh model to identify detailed feature regions on the target object; performing a second scanning process to perform high-resolution scanning on the identified detailed feature regions to obtain three-dimensional data of the detailed regions; and fusing the initial three-dimensional mesh model with the three-dimensional data of the detailed regions to generate a final three-dimensional model.

[0010] In some exemplary embodiments, the automatic identification of detailed areas includes at least one of edge identification and hole-free identification.

[0011] In some exemplary embodiments, the edge recognition process includes: setting a threshold for the included angle of the normal vectors and a first neighborhood radius; traversing the points in the mesh model and calculating the average angle between the normal vectors of the current point and the set of points within the first neighborhood radius; in response to the average angle of the normal vectors being greater than the threshold for the included angle of the normal vectors, determining the current point as an edge point; and performing smoothing optimization processing on the boundary of the edge recognition result to obtain the final edge region.

[0012] In some exemplary embodiments, the smoothing optimization process includes: setting a second neighborhood radius; traversing "non-edge points" and, in response to the fact that the set of points within the second neighborhood radius are all "non-edge points", marking the points in the corresponding region as smoothed "non-edge point 2"; and marking all "non-edge points" as edge points.

[0013] In some exemplary embodiments, the process for identifying holes without sidewalls includes: setting an edge proportion range and a weight threshold; traversing the points in the mesh model and counting the proportion of points in the neighborhood of the current point whose weight is greater than the weight threshold; determining the current point as an edge point in response to the proportion being within the edge proportion range; and identifying the area without sidewalls based on the distribution of edge points.

[0014] In some exemplary embodiments, the process for identifying holes without sidewalls further includes: setting an edge percentage threshold; traversing non-edge points and calculating the percentage of edge points in their neighborhood; and, in response to an edge point percentage greater than the edge percentage threshold, determining the corresponding non-edge point as an edge point.

[0015] In some exemplary embodiments, the fusion process employs a mesh stitching algorithm to stitch together and fuse 3D mesh data of different resolutions.

[0016] According to a second aspect of the present invention, a three-dimensional scanning device for automatically identifying detailed regions is provided, comprising: a first scanning module for performing a first scanning process to acquire an initial three-dimensional mesh model of a target object; an identification module for performing automatic detailed region identification processing based on the initial three-dimensional mesh model to identify detailed feature regions on the target object; a second scanning module for performing a second scanning process to perform high-resolution scanning on the identified detailed feature regions to acquire three-dimensional data of the detailed regions; and a fusion generation module for fusing the initial three-dimensional mesh model with the three-dimensional data of the detailed regions to generate a final three-dimensional model.

[0017] According to a third aspect of the present invention, an electronic device is provided, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0018] A fourth aspect of the present invention also provides a computer-readable storage medium having a computer program or instructions stored thereon, wherein the computer program or instructions, when executed by a processor, implement the steps of the above-described method.

[0019] (III) Beneficial Effects

[0020] As can be seen from the above technical solutions, the three-dimensional scanning method, apparatus, device, and medium for automatically identifying detailed regions provided by the embodiments of the present invention have at least the following beneficial effects:

[0021] (1) This technical solution significantly improves the automation level and work efficiency of 3D scanning through automatic recognition algorithm and multi-resolution scanning fusion mechanism. The system can automatically identify key feature areas such as edges and holes without sidewalls, replacing the traditional manual selection method that relies on human experience, effectively reducing the frequency of human-computer interaction and improving the efficiency of complex 3D scanning tasks by 40%-60%. This fully automated solution achieves seamless connection from scanning, recognition, fine scanning to fusion, reducing the professional skill requirements for operators.

[0022] (2) In terms of technical implementation, this solution constructs an edge recognition algorithm based on the calculation of the angle between normal vectors, which can accurately capture various complex edge features; at the same time, it adopts the edge proportion analysis method to effectively identify hole structures without sidewalls that are difficult to handle by traditional algorithms. These core algorithms break through the technical bottleneck of manual selection of complex structures such as long curved areas and blind spots, and demonstrate a powerful ability to model complex features.

[0023] (3) In terms of model quality, the system achieves high-precision modeling of key detail areas while maintaining overall scanning efficiency through the collaborative work of multi-resolution scanning strategy and mesh stitching algorithm. Mesh stitching technology ensures smooth transition between meshes of different resolutions and avoids problems of seams and abrupt changes in accuracy, while the secondary neighborhood optimization mechanism further improves the accuracy and smoothness of the selected area boundary.

[0024] (4) The system design emphasizes practicality and stability. The automatic recognition algorithm has good robustness and can adapt to scanning objects of different materials and complexities. The parametrically designed recognition threshold gives the system flexible adaptability and can meet the needs of various industrial inspection and 3D modeling scenarios. The complete scanning closed-loop design ensures the reliability and stability of the entire process from data acquisition to final model output. Attached Figure Description

[0025] The above-described features, other objects, and advantages of the present invention will become clearer from the following description of embodiments of the invention with reference to the accompanying drawings, in which:

[0026] Figure 1 A flowchart illustrating an automatic identification method for 3D scanning regions according to an embodiment of the present invention is shown schematically.

[0027] Figure 2 This illustration schematically shows a three-dimensional scanning device for automatically identifying detailed regions according to an embodiment of the present invention;

[0028] Figure 3 A block diagram of an electronic device illustrating an automatic identification method for three-dimensional scanning regions of detail according to an embodiment of the present invention is shown. Detailed Implementation

[0029] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the invention. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the invention for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0030] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. The terms “comprising,” “including,” etc., as used herein indicate the presence of features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0031] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0032] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0033] Figure 1 The flowchart illustrates a three-dimensional scanning method for automatically identifying detailed regions according to an embodiment of the present invention.

[0034] like Figure 1 As shown, a three-dimensional scanning method for automatically identifying detailed regions according to an embodiment of the present invention includes steps S110 to S140.

[0035] In step S110, the first scanning process is performed to obtain the initial three-dimensional mesh model of the target object.

[0036] In this step, the system completes the basic data acquisition of the target object through a standard 3D scanning process. Specifically, the system first automatically recommends or allows the operator to set an appropriate initial scanning resolution based on the object's size. Then, it acquires complete point cloud data using a 3D scanning device, followed by point cloud denoising and meshing to generate an initial 3D mesh model. The innovation of this step lies in establishing an intelligent initial parameter recommendation mechanism, whose beneficial effects are mainly reflected in three aspects: First, automatic parameter recommendation lowers the operational threshold, allowing even non-professionals to quickly get started; second, the initial scan balances efficiency and quality, laying a solid foundation for subsequent processing; and finally, the complete initial model ensures that no potentially important areas are missed in subsequent detail recognition.

[0037] In step S120, based on the initial three-dimensional mesh model, automatic identification of detailed regions is performed to identify the detailed feature regions on the target object.

[0038] In the embodiments of this application, the automatic identification processing of detailed areas includes at least one of edge identification processing and hole-free identification processing.

[0039] In some exemplary embodiments, the edge identification process includes: setting a threshold for the included angle of the normal vector and a first neighborhood radius; traversing the points in the mesh model and calculating the average angle between the normal vectors of the current point and the set of points within the first neighborhood radius; determining the current point as an edge point in response to the average angle of the normal vectors being greater than the threshold; and performing smoothing optimization on the boundary of the edge identification result to obtain the final edge region. Optionally, the smoothing optimization process includes: setting a second neighborhood radius; traversing "non-edge points"; marking the points in the corresponding region as smoothed "non-edge point 2" in response to the set of points within the second neighborhood radius being all "non-edge points"; and marking all "non-edge points" as edge points. The edge identification algorithm is implemented through a multi-level processing mechanism: first, preliminary identification is performed based on the threshold for the included angle of the normal vector, and then the identification result is optimized through secondary neighborhood analysis. The beneficial effect of this hierarchical processing is that it ensures the accuracy of identification and makes the selected area boundary smoother, reducing the pressure on subsequent mesh stitching.

[0040] In some exemplary embodiments, the process for identifying holes without sidewalls includes: setting an edge proportion range and a weight threshold; traversing the points in the mesh model and counting the proportion of points in the neighborhood of the current point whose weight is greater than the weight threshold; determining the current point as an edge point in response to the proportion being within the edge proportion range; and identifying the area without sidewalls based on the distribution of edge points. The algorithm for identifying holes without sidewalls adopts a strategy that combines edge proportion analysis with weight determination, and effectively distinguishes between real features and noise interference by setting a reasonable parameter range.

[0041] In some exemplary embodiments, the process for identifying holes without sidewalls may further include: setting an edge percentage threshold; traversing non-edge points and calculating the percentage of edge points in their neighborhood; and, in response to an edge point percentage greater than the edge percentage threshold, determining the corresponding non-edge point as an edge point.

[0042] The automatic recognition algorithm for detailed regions in this application can adapt to various complex geometric shapes and has excellent recognition capabilities for traditionally difficult features such as curved edges and irregular holes. The parameterized design allows the system to flexibly adapt to the application needs of different industries. The intelligent recognition process completely replaces manual area selection, resulting in a qualitative improvement in scanning efficiency.

[0043] In step S130, a second scanning process is performed to perform a high-resolution scan on the identified detailed feature areas to obtain the three-dimensional data of the detailed areas.

[0044] After acquiring detailed area information, the system enters the fine scanning stage. The key technical feature of this step is its ability to automatically adjust the scanning strategy based on different detailed features: a linear scanning path is used for edge features, and a circular scanning path is used for hole features. Its benefits are multifaceted: intelligent path planning ensures optimal detail capture; local high-resolution scanning reduces time costs by more than 60% compared to global high-resolution scanning; and adaptive resolution adjustment allows the system to allocate scanning resources according to feature importance, achieving an optimal balance between efficiency and quality.

[0045] In step S140, the initial 3D mesh model is fused with the 3D data of the detailed area to generate the final 3D model.

[0046] In some exemplary embodiments, the fusion process employs a mesh stitching algorithm to stitch together and fuse 3D mesh data of different resolutions.

[0047] After acquiring detailed area information, the system enters the fine scanning stage. The key technical feature of this step is its ability to automatically adjust the scanning strategy based on different detailed features: a linear scanning path is used for edge features, and a circular scanning path is used for hole features. Its benefits are multifaceted: intelligent path planning ensures optimal detail capture; local high-resolution scanning reduces time costs by more than 60% compared to global high-resolution scanning; and adaptive resolution adjustment allows the system to allocate scanning resources according to feature importance, achieving an optimal balance between efficiency and quality.

[0048] In summary, this technical solution, through the organic coordination of four steps, forms a complete 3D scanning solution. Its comprehensive benefits are reflected on three levels: technically, it overcomes the bottleneck of traditional 3D scanning where efficiency and quality are difficult to balance; in application, it significantly lowers the barrier to entry for 3D scanning technology, promoting its application in a wider range of fields; and economically, it brings significant economic benefits to users by improving efficiency and reducing labor costs. These advantages make this solution promising for applications in industrial inspection, cultural relic digitization, and reverse engineering.

[0049] Figure 2 The illustration schematically depicts a three-dimensional scanning device for automatically identifying detailed regions according to an embodiment of the present invention.

[0050] like Figure 2 As shown, the 3D scanning device 800 for automatically identifying detailed regions in this embodiment includes a first scanning module 810, an identification module 820, a second scanning module 830, and a fusion generation module 840.

[0051] The first scanning module 810 is used to perform the first scanning process and obtain the initial three-dimensional mesh model of the target object.

[0052] The recognition module 820 is used to perform automatic recognition processing of detailed regions based on the initial 3D mesh model, and to identify the detailed feature regions on the target object.

[0053] The second scanning module 830 is used to perform the second scanning process, which involves high-resolution scanning of the identified detailed feature areas to obtain three-dimensional data of the detailed areas.

[0054] The fusion generation module 840 is used to fuse the initial 3D mesh model with the 3D data of the detailed area to generate the final 3D model.

[0055] According to embodiments of the present invention, any plurality of modules among the first scanning module 810, the identification module 820, the second scanning module 830, and the fusion generation module 840 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of the present invention, at least one of the first scanning module 810, the identification module 820, the second scanning module 830, and the fusion generation module 840 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the first scanning module 810, the recognition module 820, the second scanning module 830, and the fusion generation module 840 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0056] Figure 3 A block diagram of an electronic device illustrating an automatic identification method for three-dimensional scanning regions of detail according to an embodiment of the present invention is shown.

[0057] like Figure 3As shown, an electronic device 900 according to an embodiment of the present invention includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0058] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 executes various operations of the method flow according to embodiments of the present invention by executing programs in ROM 902 and / or RAM 903. It should be noted that programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 may also execute various operations of the method flow according to embodiments of the present invention by executing programs stored in one or more memories.

[0059] According to an embodiment of the present invention, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the input / output (I / O) interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.

[0060] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0061] According to embodiments of the present invention, a computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of the present invention, a computer-readable storage medium may include ROM 902 and / or RAM 903 and / or one or more memories other than ROM 902 and RAM 903 described above.

[0062] Those skilled in the art will understand that the features described in the various embodiments of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments of the present invention can be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or combinations fall within the scope of the present invention.

Claims

1. A three-dimensional scanning method for automatically identifying detailed regions, characterized in that, include: Perform the first scan to obtain the initial 3D mesh model of the target object; Based on the initial 3D mesh model, automatic detail region recognition processing is performed to identify the detailed feature regions on the target object; A second scanning process is performed, in which a high-resolution scan is conducted on the identified detailed feature regions to obtain three-dimensional data of the detailed regions. The initial 3D mesh model is fused with the 3D data of the detailed region to generate the final 3D model.

2. The method according to claim 1, characterized in that, The automatic identification processing of detailed areas includes at least one of edge identification processing and non-sidewall hole identification processing.

3. The method according to claim 2, characterized in that, The edge recognition process includes: Set the threshold for the angle between the normal vectors and the radius of the first neighborhood; Traverse the points in the mesh model and calculate the average angle between the current point and the normal vector of the set of points within the first neighborhood radius; In response to the fact that the average angle between the normal vectors is greater than the threshold angle between the normal vectors, the current point is determined to be an edge point; The boundaries of the edge recognition results are smoothed and optimized to obtain the final edge region.

4. The method according to claim 3, characterized in that, The smoothing optimization process includes: Set the radius of the second neighborhood; Traverse the "non-edge points". In response to the fact that all points within the second neighborhood radius are "non-edge points", mark the points in the corresponding region as smooth "non-edge point 2". Mark all "non-edge points" as edge points.

5. The method according to claim 2, characterized in that, The process for identifying holes without sidewalls includes: Set the edge proportion range and weight threshold; Traverse the points in the grid model and count the percentage of points in the neighborhood of the current point whose weight is greater than the weight threshold. In response to the fact that the percentage is within the edge percentage range, the current point is determined to be an edge point; Identify areas without sidewall holes based on edge point distribution.

6. The method according to claim 5, characterized in that, The process for identifying holes without sidewalls also includes: Set an edge percentage threshold; Traverse non-edge points and count the percentage of edge points in their neighborhood; In response to the edge point proportion being greater than the edge proportion threshold, the corresponding non-edge point is determined to be an edge point.

7. The method according to claim 1, characterized in that, The fusion process employs a mesh stitching algorithm to stitch together and fuse 3D mesh data of different resolutions.

8. A three-dimensional scanning device for automatically identifying detailed areas, characterized in that, The device includes: The first scanning module is used to perform the first scanning process and obtain the initial three-dimensional mesh model of the target object; The recognition module is used to perform automatic recognition processing of detailed regions based on the initial three-dimensional mesh model, and to identify the detailed feature regions on the target object; The second scanning module is used to perform a second scanning process, which performs a high-resolution scan on the identified detailed feature region to obtain the three-dimensional data of the detailed region. The fusion generation module is used to fuse the initial 3D mesh model with the 3D data of the detailed region to generate the final 3D model.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more computer programs. The one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 7.

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