An adaptive multi-resolution laser scanning method and system

Abstract

An embodiment of the present invention provides an adaptive multi-resolution laser scanning method and system. First, the object to be scanned is scanned and collected at a fixed resolution, so that the model can be collected quickly and efficiently. Then, the automatic feature and boundary detection process is entered. Through curvature features and boundary recognition, an area of ​​interest is generated, or three-dimensional model data is loaded. By analyzing the three-dimensional model data, the area of ​​interest is automatically extracted, and the automatic resolution improvement stage is entered. The scanning is continued, thereby improving the resolution of the area of ​​interest, up to N times the fixed resolution. According to the boundary encryption points of the product, each point produced by the scanner is fully utilized to improve the resolution at the boundary position of the product, thereby providing customers with more accurate measurement analysis and visual experience of the product.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of laser technology, and in particular to an adaptive multi-resolution laser scanning method and system. Background Art

[0002] Currently, scanner generation methods are divided into fixed point spacing (fixed resolution) and subsequent manual improvement of local positions to increase spacing; however, both solutions have certain problems. Scanners mostly use a fixed resolution (i.e., fixed point spacing) method to set the resolution at the beginning of the device operation, and subsequently collect data according to the fixed resolution. Even if long-term scanning is performed, the point spacing cannot be reduced and optimized; for sheet metal / model boundaries, product corners and detail positions, fixed point spacing cannot better describe the product boundaries, resulting in a jagged effect; manually selecting positions for resolution improvement is subjectively distinguished by the operator, and then the improvement setting is performed. Manual selection of product positions is more cumbersome and cannot cope with more complex products. Summary of the Invention

[0003] An embodiment of the present invention provides an adaptive multi-resolution laser scanning method and system, which automatically encrypts points according to the boundaries of the object to be scanned, performs curvature analysis and product model analysis while collecting data, and sets areas such as boundaries and corners where the curvature exceeds a certain limit as areas of interest and automatically enhances them.

[0004] In a first aspect, an embodiment of the present invention provides an adaptive multi-resolution laser scanning method, comprising:

[0005] Step S1: After performing a preliminary scan of an object to be scanned at a preset fixed resolution, obtaining feature points of the object to be scanned, and generating a region of interest based on the feature points; the feature points include points greater than a preset curvature threshold, boundary points of the object to be scanned, and marked points;

[0006] Step S2: determining the adaptive resolution of each region of interest based on the type of feature point corresponding to the point of interest; and further scanning the region of interest based on the adaptive resolution.

[0007] Preferably, in step S1, acquiring feature points of the object to be scanned and generating a region of interest based on the feature points specifically includes:

[0008] Performing curvature feature recognition on each scanning point in the preliminary scanning result, and marking the scanning point as a feature point if it is determined that the curvature of the scanning point is greater than a preset curvature threshold;

[0009] A spatial bounding box with a fixed radius threshold is constructed based on each feature point to generate a first region of interest; and scan data within the first region of interest in the preliminary scan result is clipped.

[0010] Preferably, in step S1, acquiring feature points of the object to be scanned and generating a region of interest based on the feature points specifically includes:

[0011] The boundaries of each scanning point in the preliminary scanning result are identified, the neighborhood and normal of each scanning point are determined, and the scanning points with no connection points in the neighborhood within the preset range and whose normals are consistent with the connection points are included in the second region of interest.

[0012] Preferably, in step S1, after performing a preliminary scan of the object to be scanned based on a preset fixed resolution, the method further includes:

[0013] Loading the 3D model data of the object to be scanned, performing line recognition on the 3D model data, and aligning the 3D model data with the preliminary scan result by fitting or feature alignment methods;

[0014] Identify and analyze the three-dimensional model data to obtain corners, boundary lines and three-dimensional geometric bodies in the three-dimensional model data, and include the boundary lines, corners and three-dimensional geometric bodies in a third region of interest; the corners are line points in the three-dimensional model data whose curvature is greater than a preset curvature threshold.

[0015] Preferably, the three-dimensional geometric body includes a cylinder and a polygonal cylinder.

[0016] Preferably, in step S2, after further scanning the region of interest based on the adaptive resolution, the method further includes:

[0017] The scan data within the region of interest in the preliminary scan result is cropped, and the scan data obtained by further scanning is filled into the region of interest.

[0018] Preferably, in step S2, determining the adaptive resolution of each region of interest specifically includes:

[0019] The adaptive resolution of each region of interest is determined based on a preset resolution multiple of the region of interest corresponding to each feature point and a preset fixed resolution.

[0020] In a second aspect, an embodiment of the present invention provides an adaptive multi-resolution laser scanning system, comprising:

[0021] An initial scanning module, which performs a preliminary scan of the object to be scanned at a preset fixed resolution, obtains feature points of the object to be scanned, and generates a region of interest based on the feature points; the feature points include points with a curvature greater than a preset threshold, boundary points of the object to be scanned, and annotation points;

[0022] The adaptive scanning module determines the adaptive resolution of each region of interest based on the type of feature points corresponding to the points of interest; and further scans the region of interest based on the adaptive resolution.

[0023] In a third aspect, an embodiment of the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the adaptive multi-resolution laser scanning method as described in the embodiment of the first aspect of the present invention are implemented.

[0024] In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the adaptive multi-resolution laser scanning method as described in the embodiment of the first aspect of the present invention.

[0025] An embodiment of the present invention provides an adaptive multi-resolution laser scanning method and system, which first scans and captures the object to be scanned at a fixed resolution, allowing the model to be captured quickly and efficiently. Then, the method enters the automatic feature and boundary detection process, generates an area of ​​interest through curvature features and boundary recognition, or loads three-dimensional model data, automatically extracts the area of ​​interest by analyzing the three-dimensional model data, enters the automatic resolution improvement stage, and continues scanning, thereby improving the resolution of the area of ​​interest, up to N times the fixed resolution. The method can encrypt points based on the product's boundaries, fully utilize each point produced by the scanner, and improve the resolution at the product boundary position, thereby providing customers with more accurate measurement analysis and visual experience of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 A flowchart of an adaptive multi-resolution laser scanning method according to an embodiment of the present invention;

[0028] Figure 2 A schematic diagram of a specific process of adaptive multi-resolution laser scanning according to an embodiment of the present invention;

[0029] Figure 3 is a schematic diagram of a first region of interest according to an embodiment of the present invention;

[0030] Figure 4 A schematic diagram of a low-resolution model obtained by preliminary scanning according to an embodiment of the present invention;

[0031] Figure 5 According to an embodiment of the present invention Figure 4 Schematic diagram of corner curvature optimization;

[0032] Figure 6 is a schematic diagram of a second region of interest according to an embodiment of the present invention;

[0033] Figure 7 is a schematic diagram of a third region of interest according to an embodiment of the present invention;

[0034] Figure 8 Schematic diagram of the physical structure according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0036] In the embodiments of the present application, the term "and / or" is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone.

[0037] The terms "first" and "second" in the embodiments of the present application are only used for descriptive purposes and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present application, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a system, product or device comprising a series of components or units is not limited to the listed components or units, but may optionally also include components or units that are not listed, or may optionally also include other components or units that are inherent to these products or devices. In the description of the present application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0038] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0039] For sheet metal / model boundaries, product corners, and detail locations, fixed point spacing cannot effectively describe the product boundaries, resulting in a jagged effect. Manually selecting locations to improve resolution requires subjective judgment from the operator and then setting the improvement. This requires manual selection of product locations, which is cumbersome and cannot handle more complex products.

[0040] Therefore, embodiments of the present invention provide an adaptive multi-resolution laser scanning method and system. After scanning at a fixed resolution, this method generates a region of interest (ROI) by identifying curvature features and boundaries, thereby improving the resolution of the region of interest. This method fully utilizes every point produced by the scanner and improves the resolution at the product boundary, thereby providing customers with more accurate product measurement, analysis, and visual experience. The following describes the solutions of the present invention through multiple embodiments.

[0041] Figure 1 and Figure 2 An adaptive multi-resolution laser scanning method according to an embodiment of the present invention includes:

[0042] Step S1: After performing a preliminary scan of an object to be scanned at a preset fixed resolution, obtaining feature points of the object to be scanned, and generating a region of interest based on the feature points; the feature points include points greater than a preset curvature threshold, boundary points of the object to be scanned, and marked points;

[0043] In this embodiment, when laser scanning is performed on the object (product) to be scanned, preliminary acquisition is first performed at a fixed resolution so that the object to be scanned can be acquired quickly and efficiently. Then, an automatic detection process of feature points and boundaries is performed. By identifying curvature features and boundary features, an area of ​​interest is generated, and the resolution is increased to further scan the area of ​​interest.

[0044] Specifically, first, the laser scanner is used to perform an overall scan, and the initial scan result is quickly determined with a normal fixed resolution, that is, a low-resolution model is quickly reconstructed, such as Figure 4 As shown in Figure 5 Data after corner curvature optimization.

[0045] After the preliminary scan is completed, the automatic recognition process is entered to perform curvature feature recognition on each scanning point in the preliminary scan result. The curvature of each scanning point (3D point) is classified into high curvature and low curvature according to different thresholds. Specifically, when determining the curvature, this embodiment uses a high-speed curvature algorithm to perform curvature feature recognition. If the curvature of the scanning point is determined to be greater than the preset curvature threshold, the scanning point is marked as a feature point.

[0046] A spatial bounding box with a fixed radius threshold is constructed based on each feature point to generate the first region of interest (such as Figure 3 The scanning data within the first region of interest in the preliminary scanning result is trimmed.

[0047] In addition to identifying feature points by curvature, the boundaries of each scanning point in the preliminary scanning result can also be identified to determine the neighborhood and normal of each scanning point. The scanning points with no connection points in the neighborhood within the preset range and whose normals are consistent with the connection points are included in the second region of interest (such as Figure 6 Region of interest in 2).

[0048] In this embodiment, in addition to identifying the region of interest from the low-resolution model, the following steps are also included:

[0049] Loading the 3D model data of the object to be scanned, performing line recognition on the 3D model data, and aligning the 3D model data with the preliminary scan result by fitting or feature alignment methods;

[0050] Identify and analyze the three-dimensional model data to obtain corners, boundary lines and three-dimensional geometric bodies in the three-dimensional model data, and list the boundary lines, corners and three-dimensional geometric bodies in the third region of interest (such as Figure 7 The corner is a line point in the three-dimensional model data whose curvature is greater than a preset curvature threshold, and the three-dimensional geometric body includes a cylinder and a polygonal cylinder, or a pre-set three-dimensional feature.

[0051] like Figure 7 As shown in , by loading the known 3D model (CAD data), aligning it with the low-resolution model through best fit / feature alignment, analyzing the CAD data to find features such as corners, cylinders, boundaries, etc. in the data, and then automatically extracting the region of interest from the scanned model3.

[0052] On the basis of the above embodiments, the region of interest is not limited to the above three automatic methods, and the region of interest can be generated by manual interaction, while supporting the expansion of subsequent methods of identifying regions of interest.

[0053] Step S2: determining the adaptive resolution of each region of interest based on the type of feature point corresponding to the point of interest; and further scanning the region of interest based on the adaptive resolution.

[0054] Specifically, after completing automatic recognition, the scanner can continue scanning the area, supplementing the original data within the area, thereby improving the resolution within the area, increasing point density, and highlighting curvature and boundary features. For areas of non-interest, the resolution is fixed. For areas of interest, the scanning time is increased to collect more original points, and the resolution and point density are increased based on the curvature and boundary characteristics.

[0055] The scan data within the region of interest in the preliminary scan result is cropped, and the scan data obtained by further scanning is filled into the region of interest.

[0056] Specifically, the adaptive resolution of each ROI is determined based on the preset resolution multiple of each feature point corresponding to the ROI and the preset fixed resolution. For example, for ROI 1, increasing the scanning resolution reduces the scanning point spacing by 1 times to optimize the boundary; for ROI 2, increasing the scanning resolution reduces the scanning point spacing by 2 times to extract corners; and for ROI 3, increasing the scanning resolution reduces the scanning point spacing by 3 times to extract wireframe boundaries, cylinders, planes, etc.

[0057] An embodiment of the present invention further provides an adaptive multi-resolution laser scanning system, based on the adaptive multi-resolution laser scanning method in each of the above embodiments, comprising:

[0058] An initial scanning module, which performs a preliminary scan of the object to be scanned at a preset fixed resolution, obtains feature points of the object to be scanned, and generates a region of interest based on the feature points; the feature points include points with a curvature greater than a preset threshold, boundary points of the object to be scanned, and annotation points;

[0059] The adaptive scanning module determines the adaptive resolution of each region of interest based on the type of feature points corresponding to the points of interest; and further scans the region of interest based on the adaptive resolution.

[0060] Based on the same concept, the embodiment of the present invention also provides a schematic diagram of an entity structure, such as Figure 8As shown, the server may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the steps of the adaptive multi-resolution laser scanning method described in the above embodiments. For example, the steps include:

[0061] Step S1: After performing a preliminary scan of an object to be scanned at a preset fixed resolution, obtaining feature points of the object to be scanned, and generating a region of interest based on the feature points; the feature points include points greater than a preset curvature threshold, boundary points of the object to be scanned, and marked points;

[0062] Step S2: determining the adaptive resolution of each region of interest based on the type of feature point corresponding to the point of interest; and further scanning the region of interest based on the adaptive resolution.

[0063] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0064] Based on the same concept, an embodiment of the present invention further provides a non-transitory computer-readable storage medium storing a computer program. The computer program includes at least one code segment that can be executed by a host control device to control the host control device to implement the steps of the adaptive multi-resolution laser scanning method described in the above embodiments. For example, the steps include:

[0065] Step S1: After performing a preliminary scan of an object to be scanned at a preset fixed resolution, obtaining feature points of the object to be scanned, and generating a region of interest based on the feature points; the feature points include points greater than a preset curvature threshold, boundary points of the object to be scanned, and marked points;

[0066] Step S2: determining the adaptive resolution of each region of interest based on the type of feature point corresponding to the point of interest; and further scanning the region of interest based on the adaptive resolution.

[0067] Based on the same technical concept, an embodiment of the present application also provides a computer program, which, when executed by a main control device, is used to implement the above method embodiment.

[0068] The program may be stored in whole or in part on a storage medium packaged with the processor, or may be stored in whole or in part on a memory not packaged with the processor.

[0069] Based on the same technical concept, the embodiment of the present application further provides a processor, which is used to implement the above method embodiment. The above processor can be a chip.

[0070] In summary, the embodiments of the present invention provide an adaptive multi-resolution laser scanning method and system, which first scans and collects the object to be scanned at a fixed resolution, so that the model can be collected quickly and efficiently, and then enters the automatic detection process of features and boundaries, generates areas of interest through curvature features and boundary recognition, or loads three-dimensional model data, and automatically extracts areas of interest by analyzing the three-dimensional model data, enters the automatic resolution improvement stage, and continues scanning, thereby improving the resolution of the area of ​​interest, up to N times the fixed resolution; according to the boundary encryption points of the product, each point produced by the scanner can be fully utilized to improve the resolution at the boundary position of the product, thereby providing more accurate measurement analysis and visual experience of the customer's products.

[0071] The various embodiments of the present invention can be combined arbitrarily to achieve different technical effects.

[0072] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented 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 process or function described in this application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive).

[0073] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An adaptive multi-resolution laser scanning method, characterized in that: include: Step S1: After performing a preliminary scan of an object to be scanned at a preset fixed resolution, obtaining feature points of the object to be scanned, and generating a region of interest based on the feature points; the feature points include points greater than a preset curvature threshold, boundary points of the object to be scanned, and marked points; After the initial scan of the object based on a preset fixed resolution, it also includes: Loading the 3D model data of the object to be scanned, performing line recognition on the 3D model data, automatically extracting the region of interest by analyzing the 3D model data, and aligning the 3D model data with the preliminary scan results by fitting or feature alignment methods; Identify and analyze the three-dimensional model data to obtain corners, boundary lines, and three-dimensional geometric bodies in the three-dimensional model data, and list the boundary lines, corners, and three-dimensional geometric bodies in a third region of interest; the corners are line points in the three-dimensional model data whose curvature is greater than a preset curvature threshold; Acquiring feature points of the object to be scanned and generating a region of interest based on the feature points specifically includes: Performing curvature feature recognition on each scanning point in the preliminary scanning result, and marking the scanning point as a feature point if it is determined that the curvature of the scanning point is greater than a preset curvature threshold; Constructing a spatial bounding box with a fixed radius threshold based on each feature point to generate a first region of interest; cropping the scan data within the first region of interest in the preliminary scan result, determining an adaptive resolution for each region of interest based on a preset resolution multiple of the region of interest corresponding to each feature point and a preset fixed resolution, and filling the region of interest with scan data obtained from further scanning; Step S2: Based on the type of feature points corresponding to the points of interest, determine the adaptive resolution of each region of interest; further scan the region of interest based on the adaptive resolution, and after completing automatic recognition, the scanner continues to scan the region to supplement the original data in the region. For non-regions of interest, the resolution is fixed, the scanning time of the region of interest is increased, and the resolution is increased according to the characteristics of the curvature and boundary.

2. The adaptive multi-resolution laser scanning method according to claim 1, characterized in that: In step S1, acquiring feature points of the object to be scanned and generating a region of interest based on the feature points specifically includes: The boundaries of each scanning point in the preliminary scanning result are identified, the neighborhood and normal of each scanning point are determined, and the scanning points with no connection points in the neighborhood within the preset range and whose normals are consistent with the connection points are included in the second region of interest.

3. The adaptive multi-resolution laser scanning method according to claim 1, characterized in that: The three-dimensional geometric bodies include cylinders and polygonal cylinders.

4. The adaptive multi-resolution laser scanning method according to claim 1, characterized in that: In the step S2, after further scanning the region of interest based on the adaptive resolution, the method further includes: The scan data within the region of interest in the preliminary scan result is cropped, and the scan data obtained by further scanning is filled into the region of interest.

5. The adaptive multi-resolution laser scanning method according to claim 1, characterized in that: In step S2, determining the adaptive resolution of each region of interest specifically includes: The adaptive resolution of each region of interest is determined based on a preset resolution multiple of the region of interest corresponding to each feature point and a preset fixed resolution.

6. An adaptive multi-resolution laser scanning system, characterized in that: include: An initial scanning module, which performs a preliminary scan of the object to be scanned at a preset fixed resolution, obtains feature points of the object to be scanned, and generates a region of interest based on the feature points; the feature points include points with a curvature greater than a preset threshold, boundary points of the object to be scanned, and annotation points; An adaptive scanning module, which determines an adaptive resolution of each region of interest based on a type of feature point corresponding to the point of interest; The region of interest is further scanned based on the adaptive resolution. After automatic recognition is completed, the scanner continues to scan the region to supplement the original data in the region. For non-regions of interest, the resolution is fixed, and the scanning time of the region of interest is increased, and the resolution is increased according to the characteristics of the curvature and boundary.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the adaptive multi-resolution laser scanning method according to any one of claims 1 to 6 are implemented.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the adaptive multi-resolution laser scanning method according to any one of claims 1 to 7 are implemented.

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