Three-dimensional data acquisition method and apparatus, computer device, and storage medium

Abstract

The application relates to a three-dimensional data acquisition method and device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring a laser plane and at least two synchronous two-dimensional images based on a three-dimensional scanning device, and determining candidate laser segments in each two-dimensional image; each two-dimensional image corresponds to an image acquisition device of the three-dimensional scanning device; the candidate laser segments in the at least two two-dimensional images are matched under limit constraint to obtain candidate segment pairs; each pair of candidate segment pairs is subjected to three-dimensional reconstruction to obtain corresponding candidate three-dimensional point sets; the candidate three-dimensional point sets are subjected to distance evaluation based on the laser plane, and a target three-dimensional point set is determined according to an evaluation result. Through the application, the matching calculation data volume is greatly reduced, the influence on the scanning running frame rate is reduced, and the scanning efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to methods, apparatus, computer equipment and storage media for acquiring three-dimensional data. Background Technology

[0002] With the continuous development of sensor technology, laser 3D scanning equipment has been widely used in the automotive and aerospace industries due to its high accuracy and resolution. Laser 3D scanning equipment typically uses the triangulation principle to determine the 3D data of the object being measured.

[0003] Current 3D data acquisition methods involve using a laser 3D scanning device to project multiple laser lines onto the surface of the object being measured. Different cameras within the laser 3D scanning device acquire 2D images. Then, all laser line segments in each 2D image are paired, and the 3D data is calculated based on the pairing results. The drawback of this method is that it requires matching all laser line segments in each 2D image. Assuming the laser 3D scanning device uses n laser lines, the first 2D image captured by the first camera will show n laser lines, and the second 2D image captured by the second camera will also show n laser lines. Simultaneously, n laser planes are generated during calibration. A given laser line in the first 2D image has n possible matches in the second 2D image. Each match requires reconstructing a 3D point, which is then compared with the n laser planes. This results in a total of n*n*n calculations. As the number of laser lines increases, the amount of matching calculation data becomes enormous, severely impacting the scanning frame rate and leading to low scanning efficiency.

[0004] There is currently no effective solution to the problem that the matching calculation data is huge, which seriously affects the scanning frame rate and leads to low scanning efficiency in related technologies. Summary of the Invention

[0005] This embodiment provides a three-dimensional data acquisition method, apparatus, computer device, and storage medium to solve the problem in related technologies where the amount of matching calculation data is huge, which seriously affects the scanning frame rate and leads to low scanning efficiency.

[0006] Firstly, this embodiment provides a three-dimensional data acquisition method, applied in a three-dimensional scanning device, the method comprising:

[0007] The laser plane and at least two synchronized two-dimensional images are acquired using a three-dimensional scanning device, and candidate laser segments are determined in each of the two-dimensional images; each of the two-dimensional images corresponds to an image acquisition device of the three-dimensional scanning device;

[0008] Candidate laser segments from at least two of the two-dimensional images are matched under limit constraints to obtain candidate segment pairs;

[0009] Each pair of candidate segments is reconstructed in three dimensions to obtain the corresponding candidate three-dimensional point set;

[0010] The distance to the candidate 3D point set is evaluated based on the laser plane, and the target 3D point set is determined based on the evaluation results.

[0011] In some embodiments, the step of evaluating the distance of the candidate 3D point set based on the laser plane and determining the target 3D point set based on the evaluation result includes:

[0012] Determine the distance from each candidate 3D point in the candidate 3D point set to each laser plane, and use the minimum distance as the screening criterion to determine the real laser plane corresponding to the candidate 3D point;

[0013] When the distance between the candidate 3D point and the real laser plane satisfies a preset threshold, the candidate 3D point is determined as the target 3D point set.

[0014] In some embodiments, determining the distance from each candidate 3D point in the candidate 3D point set to each laser plane, and using the minimum distance as the screening criterion to determine the real laser plane corresponding to the candidate 3D point, includes:

[0015] The laser planes are numbered according to their order, and the starting point number, intermediate number, and ending point number are determined.

[0016] Determine the distance relationship between each candidate 3D point in the candidate 3D point set and the laser plane with the starting point number, intermediate number, and ending point number, respectively;

[0017] Determine whether the distance relationship satisfies the convergence condition;

[0018] When the distance relationship meets the convergence condition, the minimum distance is used as the screening criterion to determine the real laser plane corresponding to each candidate 3D point in the candidate 3D point set.

[0019] In some embodiments, before performing distance evaluation on the candidate 3D point set based on the laser plane and determining the target 3D point set based on the evaluation result, the method further includes:

[0020] The laser planes are numbered according to their order.

[0021] The candidate 3D point set is filtered according to the number of the laser plane.

[0022] In some embodiments, numbering the laser planes according to their order includes:

[0023] According to the order of the laser planes, the laser plane at the starting position is numbered as the starting number, and the laser plane at the ending position is numbered as the ending number.

[0024] In some embodiments, filtering the candidate 3D point set according to the number of the laser plane includes:

[0025] Using the starting point number and the ending point number as the critical interval, traverse the relationship between each candidate 3D point in the candidate 3D point set and the critical interval;

[0026] When a candidate 3D point falls into the critical interval, the candidate 3D point is retained.

[0027] If a candidate 3D point does not fall within the critical interval, then the candidate 3D point is eliminated.

[0028] In some embodiments, the step of performing limit-constrained matching of candidate laser segments in at least two synchronized two-dimensional images to obtain candidate segment pairs includes:

[0029] Obtain the intrinsic and extrinsic parameters of the 3D scanning system;

[0030] Based on the aforementioned intrinsic and extrinsic parameters, limit constraint matching is performed between candidate laser segments in at least two synchronized two-dimensional images according to the principle of limit constraints to obtain corresponding candidate segment pairs.

[0031] In some embodiments, the step of matching candidate laser segments in at least two synchronized two-dimensional images based on the intrinsic and extrinsic parameters and according to the principle of limit constraints to obtain corresponding candidate segment pairs includes:

[0032] Two-dimensional feature points are extracted from candidate laser segments in at least two synchronized two-dimensional images, respectively;

[0033] Based on the aforementioned intrinsic and extrinsic parameters, limit constraint matching is performed between the two-dimensional feature points corresponding to the two-dimensional image according to the principle of limit constraint to obtain matching point pairs.

[0034] Based on the matching point pairs, the corresponding candidate segment pairs are determined.

[0035] In some embodiments, determining candidate laser segments in each of the two-dimensional images includes:

[0036] Feature recognition is performed on the two-dimensional image to obtain laser feature segments;

[0037] Based on the laser plane, the laser feature segments are screened to obtain candidate laser segments.

[0038] Secondly, this embodiment provides a three-dimensional data acquisition device, which is applied in a three-dimensional scanning device. The device includes: an acquisition module, a matching module, a reconstruction module, and an evaluation module.

[0039] The acquisition module is used to acquire a laser plane and at least two synchronized two-dimensional images based on a three-dimensional scanning device, and to determine candidate laser segments in each of the two-dimensional images; each of the two-dimensional images corresponds to an image acquisition device of the three-dimensional scanning device;

[0040] The matching module is used to perform limit constraint matching on candidate laser segments in at least two of the two-dimensional images to obtain candidate segment pairs;

[0041] The reconstruction module is used to perform three-dimensional reconstruction on each pair of candidate fragments to obtain the corresponding candidate three-dimensional point set;

[0042] The evaluation module is used to evaluate the distance of the candidate 3D point set based on the laser plane, and determine the target 3D point set based on the evaluation result.

[0043] Thirdly, this embodiment provides a three-dimensional scanning device, including at least two synchronously configured image acquisition devices, at least one laser projector, and a processor;

[0044] The at least one laser projector is used to simultaneously project at least two laser lines onto the object to be scanned.

[0045] The at least two image acquisition devices are connected to the processor and are used to synchronously acquire two-dimensional images of the object to be scanned.

[0046] The processor is used to execute the three-dimensional data acquisition method described in the first aspect.

[0047] Fourthly, this embodiment provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the three-dimensional data acquisition method described in the first aspect above.

[0048] Fifthly, this embodiment provides a storage medium storing a computer program that, when executed by a processor, implements the three-dimensional data acquisition method described in the first aspect above.

[0049] Compared with related technologies, the three-dimensional data acquisition method, apparatus, computer equipment, and storage medium provided in this embodiment acquire a laser plane and at least two synchronized two-dimensional images based on a three-dimensional scanning device, and determine candidate laser segments in each two-dimensional image; each two-dimensional image corresponds to an image acquisition device of a three-dimensional scanning device; the candidate laser segments in at least two two-dimensional images are matched with limit constraints to obtain candidate segment pairs; each pair of candidate segments is reconstructed in three dimensions to obtain a corresponding candidate three-dimensional point set; distance evaluation is performed on the candidate three-dimensional point set based on the laser plane, and the target three-dimensional point set is determined according to the evaluation results. This solves the problem of the huge amount of matching calculation data, which seriously affects the scanning frame rate and leads to low scanning efficiency. By using limit constraint matching, the amount of calculation of candidate laser segments can be greatly reduced. Combined with distance evaluation of the candidate three-dimensional point set based on the laser plane to quickly lock the target three-dimensional point, the amount of matching calculation data is greatly reduced, the impact on the scanning frame rate is reduced, and thus the scanning efficiency is improved.

[0050] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0051] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0052] Figure 1 This is a hardware structure block diagram of a three-dimensional scanning device provided in an embodiment of this application;

[0053] Figure 2 This is a flowchart of a three-dimensional data acquisition method provided in an embodiment of this application;

[0054] Figure 3 yes Figure 2 Flowchart of step S240;

[0055] Figure 4 This is a structural block diagram of a three-dimensional data acquisition device provided in an embodiment of this application.

[0056] In the figure: 11, image acquisition device; 12, laser projector; 13, processor; 210, acquisition module; 220, matching module; 230, reconstruction module; 240, evaluation module. Detailed Implementation

[0057] To better understand the purpose, technical solution, and advantages of this application, the application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0058] Unless otherwise defined, the technical or scientific terms used in this application shall have the general meaning as understood by one of ordinary skill in the art to which this application pertains. Words such as “a,” “an,” “an,” “the,” “the,” and “these,” used in this application, do not indicate quantitative limitation and may be singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include steps or modules (units) not listed, or may include other steps or modules (units) inherent to such processes, methods, products, or devices. The terms “connected,” “linked,” and “coupled,” used in this application, are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. The term “multiple” used in this application refers to two or more. The "and / or" operator describes the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: A alone, A and B simultaneously, and B alone. Typically, the character " / " indicates that the objects before and after it are in an "or" relationship. The terms "first," "second," and "third," etc., used in this application are merely for distinguishing similar objects and do not represent a specific ordering of the objects.

[0059] Figure 1 This is a schematic diagram of the structure of an exemplary three-dimensional scanning device according to some embodiments of the present invention. (Reference) Figure 1 As shown, the three-dimensional scanning device includes: at least two synchronously configured image acquisition devices 11, at least one laser projector 12, and a processor 13; at least one laser projector 12 is used to simultaneously project at least two laser lines onto the object to be scanned; at least two image acquisition devices 11 are connected to the processor 13 and are used to synchronously acquire two-dimensional images of the object to be scanned; the processor 13 is used to execute the steps of the three-dimensional data acquisition method in the following embodiments.

[0060] The image acquisition device 11 can be a camera, webcam, scanner, etc. When there are two image acquisition devices 11, their synchronous setup means that after calibration, they can synchronously acquire two-dimensional images, and the two acquired two-dimensional images can be considered as the same frame. In this case, the image acquisition device 11 can be considered as a stereo camera, capable of matching relevant stereo algorithms. If there are three or more image acquisition devices 11, they also need to be synchronously set in a stereo configuration.

[0061] The laser projector 12 can be one or more laser projectors 12; each laser projector 12 can project laser lines simultaneously; these laser lines are parallel or intersecting; each laser line can be a straight line or a curve. When there are two laser projectors 12, each laser projector 12 can project one laser line. In other embodiments, the laser projector 12 may include a linear laser element and a beam splitter; the laser line emitted by the linear laser element is split into multiple laser lines by the beam splitter.

[0062] The processor 13 may include, but is not limited to, a microprocessor (MCU) or a programmable logic device (FPGA). The processing device may include one or more processing modules and a memory for storing data. The memory may be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the three-dimensional data acquisition method in this embodiment. The processing module executes various functional applications and data processing by running the computer program stored in the memory, thereby implementing the aforementioned method. The memory may include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory may further include memory remotely located relative to the processing module, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks (LANs), mobile communication networks, and combinations thereof.

[0063] In some embodiments, the 3D scanning device also includes a supplementary light;

[0064] A supplementary light, connected to the processor 13, is used to provide supplementary light to the image acquisition device 11 when the image acquisition device 11 acquires a two-dimensional image that does not contain laser lines.

[0065] Specifically, the supplementary light can be an LED light array. When the image acquisition device 11 acquires a two-dimensional image that does not contain laser lines, the supplementary light is turned on to illuminate the object to be scanned, providing supplementary light for the image acquisition device 11, which can improve the clarity of the two-dimensional image in this embodiment.

[0066] In some embodiments, the 3D scanning device may further include a handheld terminal and a remote computing terminal; the image acquisition device 11 and the laser projector 12 are integrated into the handheld terminal and connected wirelessly or wiredly to the processor 13 in the remote computing terminal. This separate structure reduces the weight of the handheld terminal, facilitating handheld scanning. In other embodiments, the 3D scanning device may also include triggers, etc., which will not be described in detail here.

[0067] This embodiment provides a method for acquiring three-dimensional data. Figure 2This is a flowchart of the three-dimensional data acquisition method in this embodiment, as follows: Figure 2 As shown, the process includes the following steps:

[0068] Step S210: Acquire the laser plane and at least two synchronized two-dimensional images based on the three-dimensional scanning device, and determine the candidate laser segments in each two-dimensional image; each two-dimensional image corresponds to an image acquisition device of the three-dimensional scanning device;

[0069] Step S220: Match candidate laser segments in at least two two-dimensional images under limit constraints to obtain candidate segment pairs;

[0070] Step S230: Perform 3D reconstruction on each pair of candidate fragments to obtain the corresponding candidate 3D point set;

[0071] Step S240: Evaluate the distance of the candidate 3D point set based on the laser plane, and determine the target 3D point set based on the evaluation results.

[0072] Specifically, the laser plane is obtained after the 3D scanning equipment is calibrated. The laser plane corresponds to the laser line emitted by the 3D scanning equipment, with one laser plane corresponding to one laser line; and the light plane equation of each laser plane is known.

[0073] In the multi-laser-line scanning process, each image acquisition device acquires a single two-dimensional image synchronously. Since the laser lines are broken by the surface of the object being scanned, each two-dimensional image presents a laser fragment. Feature processing is performed on each two-dimensional image to determine candidate laser fragments. This feature processing includes, but is not limited to, feature recognition, feature extraction, sub-pixel processing, and feature filtering (removing interfering fragments), all of which can be implemented using relevant algorithms and will not be described in detail here.

[0074] The principle of limit constraints states that the projection of any point in space onto the image plane must lie on the epipolar plane formed by that point and the centers of the two cameras. For a feature point in a two-dimensional image (first camera), its matching point in another two-dimensional image (second camera) must lie on the corresponding epipolar line; this is called epipolar constraint. Therefore, based on this principle, matching candidate laser segments from at least two two-dimensional images using limit constraints yields candidate segment pairs, which accelerates computation. Then, using each pair of candidate segments as the basis for 3D reconstruction, a 3D reconstruction algorithm is employed, combined with the camera's intrinsic and extrinsic parameters, to reconstruct the corresponding candidate 3D point set. Finally, distance evaluation is performed on the candidate 3D point set based on the laser plane. This distance evaluation includes the real laser plane corresponding to each candidate 3D point set and the distance to the real laser plane; thus, the target 3D point set is quickly located based on the evaluation results.

[0075] Through the above steps, firstly, a laser plane and at least two synchronized two-dimensional images are acquired based on a three-dimensional scanning device, and candidate laser segments are determined in each two-dimensional image; each two-dimensional image corresponds to an image acquisition device of a three-dimensional scanning device; secondly, the candidate laser segments in at least two two-dimensional images are matched with limit constraints to obtain candidate segment pairs; using limit constraint matching can greatly reduce the computational amount of candidate laser segments; thirdly, each pair of candidate segments is reconstructed in three dimensions to obtain the corresponding candidate three-dimensional point set; finally, the distance of the candidate three-dimensional point set is evaluated based on the laser plane, and the target three-dimensional point is quickly locked according to the evaluation result. This greatly reduces the amount of matching calculation data, reduces the impact on the scanning frame rate, and thus improves scanning efficiency; it solves the problem that the large amount of matching calculation data seriously affects the scanning frame rate, resulting in low scanning efficiency.

[0076] Step S220 above can be considered as a coarse matching process, which will be described in detail below:

[0077] In some embodiments, step S220, which involves performing limit constraint matching on candidate laser segments from at least two synchronized two-dimensional images to obtain candidate segment pairs, includes the following steps:

[0078] Step S221: Obtain the intrinsic and extrinsic parameters of the 3D scanning system;

[0079] Step S222: Based on intrinsic and extrinsic parameters, limit constraint matching is performed between candidate laser segments in at least two synchronized two-dimensional images according to the limit constraint principle to obtain corresponding candidate segment pairs.

[0080] Specifically, the intrinsic and extrinsic parameters of a 3D scanning system include camera intrinsic parameters and camera extrinsic parameters. The camera intrinsic parameters are the camera's internal parameters, including focal length f, the position of the principal point p, and the size ratio of pixels to the real environment; these are preset. The camera extrinsic parameters include the rotation matrix R and the translation matrix T; these parameters can be obtained through calibration.

[0081] Limit constraint matching uses candidate laser segments from every two 2D images as the processing object. It can be considered that a laser segment in one 2D image is used as a reference to match a laser segment in the other 2D image, resulting in a series of matching candidate segment pairs. This significantly reduces the amount of matching during the coarse matching process, thus quickly and accurately selecting candidate segment pairs.

[0082] In some embodiments, step S222, which involves matching candidate laser segments in at least two synchronized two-dimensional images based on intrinsic and extrinsic parameters and according to the principle of limit constraints to obtain corresponding candidate segment pairs, includes the following steps:

[0083] Two-dimensional feature points are extracted from candidate laser segments in at least two synchronized two-dimensional images;

[0084] Based on intrinsic and extrinsic parameters, limit constraint matching is performed between corresponding two-dimensional feature points in a two-dimensional image according to the principle of limit constraint to obtain matching point pairs.

[0085] Based on the matching point pairs, the corresponding candidate segment pairs are determined.

[0086] Specifically, if the two-dimensional image acquired by the first camera is taken as the first two-dimensional image, and the image acquired by the second camera is taken as the second two-dimensional image, then through feature recognition and feature extraction algorithms, corresponding two-dimensional feature points are extracted from the candidate laser segments in the first and second two-dimensional images, respectively. These two-dimensional feature points can represent the candidate laser segments in the corresponding two-dimensional images. Then, based on intrinsic and extrinsic parameters, limit constraint matching is performed between the two two-dimensional feature points according to the limit constraint principle to obtain matching point pairs. Each matching point pair includes matching points located in the first and second two-dimensional images. By traversing the matching point pairs, the corresponding candidate segment pairs can be determined.

[0087] In this embodiment, by matching the limit constraints between two-dimensional feature points, candidate fragment pairs are determined, which can further improve the accuracy of candidate fragment pairs.

[0088] At this point, the coarse matching process is complete. Next, each pair of candidate segments is reconstructed in 3D to obtain the corresponding candidate 3D point set. Based on this candidate 3D point set, a fine matching process, as described in step S240, is then performed. The fine matching process is explained in detail below:

[0089] In some of these embodiments, such as Figure 3 As shown, step S240, which involves evaluating the distance to the candidate 3D point set based on the laser plane and determining the target 3D point set based on the evaluation results, includes the following steps:

[0090] Step S241: Determine the distance from each candidate 3D point in the candidate 3D point set to each laser plane, and use the minimum distance as the screening criterion to determine the real laser plane corresponding to the candidate 3D point;

[0091] Step S242: When the distance between the candidate 3D point and the real laser plane meets the preset threshold, the candidate 3D point is determined as the target 3D point set.

[0092] The candidate 3D point set contains a large number of candidate 3D points; distance evaluation can be performed by traversing all candidate 3D points in the serial or parallel candidate 3D point set, and the target 3D point set can be determined based on the evaluation results.

[0093] Specifically, taking a candidate 3D point from the candidate 3D point set as the object, the distance from the candidate 3D point to each laser plane is calculated, obtaining a corresponding number of distance values. The laser plane corresponding to the smallest distance value is selected as the real laser plane. At this point, there is a real laser plane relative to the candidate 3D point. It is also necessary to compare the distance between the candidate 3D point and the real laser plane with a preset threshold to determine whether the candidate 3D point is the target 3D point (relative to the real target 3D point). Only when the distance between the candidate 3D point and the real laser plane meets the preset threshold is the candidate 3D point retained; if the distance does not meet the preset threshold, the candidate 3D point is removed. After processing each candidate 3D point in all candidate 3D point sets, the target 3D point set is obtained. This reduces the probability of false positives, thereby improving accuracy.

[0094] In some embodiments, to reduce the complexity of the inspection and improve its efficiency while ensuring accuracy, step S241 involves determining the distance from each candidate 3D point in the candidate 3D point set to each laser plane, and using the minimum distance as the screening criterion to determine the real laser plane corresponding to the candidate 3D point. This includes the following steps:

[0095] The laser planes are numbered according to their order, and the starting point number, intermediate number, and ending point number are determined.

[0096] Determine the distance relationship between each candidate 3D point in the candidate 3D point set and the laser plane with the starting point number, intermediate point number, and ending point number, respectively;

[0097] Determine whether the distance relationship satisfies the convergence condition;

[0098] When the distance relationship meets the convergence condition, the minimum distance is used as the screening criterion to determine the real laser plane corresponding to each candidate 3D point in the candidate 3D point set.

[0099] Specifically, for each laser plane and one laser line, the laser plane corresponding to the laser line on one side (left end) of the object to be scanned is selected as the starting point for sorting, and numbers are generated from the left end (one side) to the right end (the other side); thus obtaining the starting point number, intermediate number, and ending point number. The intermediate number corresponds to the laser plane corresponding to the middle laser line. If the number of laser planes is odd, for example, 7 laser planes, starting with 0 for numbering, then the third laser plane is used as the intermediate number, and its number is 3. If the number of laser planes is even, for example, 8 laser planes, starting with 0 for numbering, then the third laser plane (closest to the starting point) is used as the intermediate number, and its number is 3. In other embodiments, the sorting starting point, intermediate number, and ending point number can be set according to the actual application situation, which will not be elaborated here.

[0100] Next, calculate the distance between each candidate 3D point in the candidate 3D point set and the laser planes at the starting point number, intermediate point number, and ending point number. At this point, each candidate 3D point has three distance values. The sign of the distance value determines the distance relationship, which represents the left-right position relationship between the candidate 3D point and the laser plane. For example, if the distance from candidate 3D point a to laser plane_1 is 0.253, the distance from candidate 3D point a to laser plane_2 is -0.247, and the distance from candidate 3D point a to laser plane_3 is -0.733, then we can consider candidate 3D point a to the right of laser plane_1 and to the left of laser planes_2 and_3. Furthermore, we can determine that candidate 3D point a is between laser planes_1 and_2.

[0101] The convergence condition refers to the requirement that a candidate 3D point has a distance relationship with only two laser planes. For example, if candidate 3D point 'a' is located between laser planes 'plane_1' and 'plane_2', and there are no other laser planes between them, then the distance relationship is considered to meet the convergence condition. The minimum distance is used as the selection criterion to determine the true laser plane corresponding to each candidate 3D point in the candidate 3D point set. That is, the distance from candidate 3D point 'a' to laser plane 'plane_2' is the minimum -0.247, therefore, the true laser plane corresponding to candidate 3D point 'a' is considered to be laser plane 'plane_2'.

[0102] In another embodiment, a binary search method can also be used to quickly complete the verification, as described below.

[0103] Assume there are 9 laser planes (corresponding to 9 laser lines); starting with 0 as the numbering; then the starting point is numbered plane_0, the middle point is numbered plane_4, and the ending point is numbered plane_8; for a candidate 3D point b, calculate the distance values ​​from the candidate 3D point b to the laser planes plane_0, plane_4, and plane_8; based on the sign relationship (i.e., left-right relationship) of the distance values, determine the distance relationship between the candidate 3D point b and each laser plane, and thus determine the specific interval where the candidate 3D point b is located; if the distance relationship between the candidate 3D point b and plane_0 has the opposite sign to the distance relationship between the candidate 3D point b and plane_4, then it can be considered that the candidate 3D point b is between plane_0 and plane_4;

[0104] Then calculate the distance between the candidate 3D point b and plane_0, plane_2, and plane_4, repeat the above steps, and gradually narrow down the range until the convergence condition is met, and then determine the true laser plane.

[0105] In some embodiments, before evaluating the distance to the candidate 3D point set based on the laser plane and determining the target 3D point set based on the evaluation results, the following steps are also included:

[0106] The laser planes are numbered according to their order.

[0107] Candidate 3D point sets are filtered based on the laser plane number.

[0108] Specifically, the laser planes are numbered according to their order using the same method as described above, and will not be repeated here. Then, the laser plane numbers are used to filter the candidate 3D points in the candidate 3D point set, quickly eliminating candidate 3D points located outside the starting point number and station, thereby further reducing the amount of data processing.

[0109] The process of numbering the laser planes according to their order includes the following steps:

[0110] According to the order of the laser planes, the laser plane at the starting position is numbered as the starting number, and the laser plane at the ending position is numbered as the ending number.

[0111] In this embodiment, only two laser planes need to be numbered: the starting point number and the ending point number. These numbers can also be directly applied to the distance evaluation step. In other embodiments, intermediate numbering can also be completed directly; then, in the distance evaluation step, the process of determining the distance relationship between each candidate 3D point in the candidate 3D point set and the laser planes with the starting point number, intermediate number, and ending point number can be directly executed, reducing computational redundancy.

[0112] The process of filtering candidate 3D point sets based on the laser plane number includes the following steps:

[0113] Using the starting point number and the ending point number as the critical interval, traverse the relationship between each candidate 3D point in the candidate 3D point set and the critical interval;

[0114] If a candidate 3D point falls into the critical interval, the candidate 3D point is retained.

[0115] If a candidate 3D point does not fall into the critical interval, the candidate 3D point is removed.

[0116] Specifically, using the starting point number and ending point number as the critical interval, we iterate through the relationship between each candidate 3D point in the candidate 3D point set and the critical interval. For example, if candidate 3D point c is within the critical interval, then candidate 3D point c is retained in the candidate 3D point set; if candidate 3D point d is outside the critical interval, then candidate 3D point d is removed from the candidate 3D point set. The relationship between each candidate 3D point and the critical interval can be determined by calculating the distance relationship, which will not be repeated here.

[0117] In some embodiments, determining candidate laser segments in each two-dimensional image in step S210 includes the following steps:

[0118] Feature recognition is performed on the two-dimensional image to obtain laser feature fragments;

[0119] Candidate laser segments are obtained by filtering laser feature segments based on the laser plane.

[0120] Specifically, relevant algorithms are used to perform feature recognition on the two-dimensional image to obtain laser feature segments; then, based on the light plane equation of the laser plane, laser feature segments within the range of the light plane equation are selected to obtain candidate laser segments.

[0121] In this embodiment, a large amount of interfering data can be eliminated through feature recognition and laser plane-based screening.

[0122] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0123] This embodiment also provides a three-dimensional data acquisition device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. The terms "module," "unit," "subunit," etc., used below refer to combinations of software and / or hardware that perform a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0124] Figure 4 This is a structural block diagram of the three-dimensional data acquisition device in this embodiment, as shown below. Figure 4 As shown, the device includes: an acquisition module 210, a matching module 220, a reconstruction module 230, and an evaluation module 240;

[0125] The acquisition module 210 is used to acquire a laser plane and at least two synchronized two-dimensional images based on a three-dimensional scanning device, and to determine candidate laser segments in each two-dimensional image; each two-dimensional image corresponds to an image acquisition device of a three-dimensional scanning device;

[0126] The matching module 220 is used to perform limit constraint matching on candidate laser segments in at least two two-dimensional images to obtain candidate segment pairs;

[0127] The reconstruction module 230 is used to perform three-dimensional reconstruction on each pair of candidate fragments to obtain the corresponding candidate three-dimensional point set;

[0128] Evaluation module 240 is used to evaluate the distance of candidate 3D point sets based on the laser plane, and determine the target 3D point set based on the evaluation results.

[0129] The above-mentioned device solves the problem of the large amount of matching calculation data, which seriously affects the scanning frame rate and leads to low scanning efficiency. By using limit constraint matching, the amount of calculation of candidate laser segments can be greatly reduced. Combined with distance evaluation of candidate 3D point sets based on the laser plane to quickly lock the target 3D point, the amount of matching calculation data is greatly reduced, the impact on the scanning frame rate is reduced, and the scanning efficiency is improved.

[0130] In some embodiments, the evaluation module 240 is further configured to determine the distance from each candidate 3D point in the candidate 3D point set to each laser plane, and to determine the real laser plane corresponding to the candidate 3D point based on the minimum distance.

[0131] When the distance between the candidate 3D point and the real laser plane meets a preset threshold, the candidate 3D point is determined as the target 3D point set.

[0132] In some embodiments, the evaluation module 240 is also used to number the laser planes according to the order of the laser planes, and determine the starting point number, the intermediate number and the ending point number.

[0133] Determine the distance relationship between each candidate 3D point in the candidate 3D point set and the laser plane with the starting point number, intermediate point number, and ending point number, respectively;

[0134] Determine whether the distance relationship satisfies the convergence condition;

[0135] When the distance relationship meets the convergence condition, the minimum distance is used as the screening criterion to determine the real laser plane corresponding to each candidate 3D point in the candidate 3D point set.

[0136] In some embodiments, the three-dimensional data acquisition device further includes a filtering module;

[0137] The filtering module is used to number the laser planes according to their sorting order.

[0138] Candidate 3D point sets are filtered based on the laser plane number.

[0139] In some embodiments, the filtering module is further configured to number the laser plane at the starting position as the starting point number and the laser plane at the ending position as the ending point number according to the order of the laser planes.

[0140] In some embodiments, the filtering module is further configured to traverse the relationship between each candidate 3D point in the candidate 3D point set and the critical interval, using the start point number and the end point number as the critical interval.

[0141] If a candidate 3D point falls into the critical interval, the candidate 3D point is retained.

[0142] If a candidate 3D point does not fall into the critical interval, the candidate 3D point is removed.

[0143] In some embodiments, the matching module 220 is also used to acquire the intrinsic and extrinsic parameters of the 3D scanning system;

[0144] Based on intrinsic and extrinsic parameters, limit constraint matching is performed between candidate laser segments in at least two synchronized two-dimensional images according to the principle of limit constraints to obtain corresponding candidate segment pairs.

[0145] In some embodiments, the matching module 220 is further configured to extract two-dimensional feature points from candidate laser segments in at least two synchronized two-dimensional images, respectively.

[0146] Based on intrinsic and extrinsic parameters, limit constraint matching is performed between corresponding two-dimensional feature points in a two-dimensional image according to the principle of limit constraint to obtain matching point pairs.

[0147] Based on the matching point pairs, the corresponding candidate segment pairs are determined.

[0148] In some embodiments, the acquisition module 210 is also used to perform feature recognition on the two-dimensional image to obtain laser feature fragments;

[0149] Candidate laser segments are obtained by filtering laser feature segments based on the laser plane.

[0150] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0151] This embodiment also provides a computer device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0152] Optionally, the computer device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0153] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0154] S1, acquire the laser plane and at least two synchronized two-dimensional images based on the three-dimensional scanning device, and determine the candidate laser segments in each two-dimensional image; each two-dimensional image corresponds to an image acquisition device of the three-dimensional scanning device;

[0155] S2, perform limit constraint matching on candidate laser segments in at least two two-dimensional images to obtain candidate segment pairs;

[0156] S3, perform 3D reconstruction on each pair of candidate segments to obtain the corresponding candidate 3D point set;

[0157] S4. Evaluate the distance between the candidate 3D point set and the laser plane, and determine the target 3D point set based on the evaluation results.

[0158] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated in this embodiment.

[0159] Furthermore, in conjunction with the three-dimensional data acquisition method provided in the above embodiments, this embodiment can also provide a storage medium for implementation. The storage medium stores a computer program; when executed by a processor, the computer program implements any of the three-dimensional data acquisition methods described in the above embodiments.

[0160] It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. All other embodiments derived by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0161] Obviously, the accompanying drawings are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar situations based on these drawings without any creative effort. Furthermore, it is understood that although the work done in this development process may be complex and lengthy, for those skilled in the art, certain design, manufacturing, or production modifications made based on the technical content disclosed in this application are merely conventional technical means and should not be considered as insufficient disclosure of this application.

[0162] The term "embodiment" in this application refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply that it is mutually exclusive with or independent of other embodiments. It will be clearly or implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0163] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A three-dimensional data acquisition method, applied in a three-dimensional scanning device, characterized in that, The method includes: The laser plane and at least two synchronized two-dimensional images are acquired using a three-dimensional scanning device, and candidate laser segments are determined in each of the two-dimensional images; each of the two-dimensional images corresponds to an image acquisition device of the three-dimensional scanning device; Candidate laser segments from at least two of the two-dimensional images are matched under limit constraints to obtain candidate segment pairs; Each pair of candidate segments is reconstructed in three dimensions to obtain the corresponding candidate three-dimensional point set; The distance to the candidate 3D point set is evaluated based on the laser plane, and the target 3D point set is determined based on the evaluation result, which includes: Determine the distance from each candidate 3D point in the candidate 3D point set to each laser plane, and use the minimum distance as the screening criterion to determine the real laser plane corresponding to the candidate 3D point; When the distance between the candidate 3D point and the real laser plane satisfies a preset threshold, the candidate 3D point is determined as the target 3D point set.

2. The three-dimensional data acquisition method according to claim 1, characterized in that, The step of determining the distance from each candidate 3D point in the candidate 3D point set to each laser plane, and using the minimum distance as the screening criterion, to determine the real laser plane corresponding to the candidate 3D point includes: The laser planes are numbered according to their order, and the starting point number, intermediate number, and ending point number are determined. Determine the distance relationship between each candidate 3D point in the candidate 3D point set and the laser plane with the starting point number, intermediate number, and ending point number, respectively; Determine whether the distance relationship satisfies the convergence condition; When the distance relationship meets the convergence condition, the minimum distance is used as the screening criterion to determine the real laser plane corresponding to each candidate 3D point in the candidate 3D point set.

3. The three-dimensional data acquisition method according to claim 1, characterized in that, Before performing distance evaluation on the candidate 3D point set based on the laser plane and determining the target 3D point set based on the evaluation result, the method further includes: The laser planes are numbered according to their order. The candidate 3D point set is filtered according to the number of the laser plane.

4. The three-dimensional data acquisition method according to claim 3, characterized in that, The step of numbering the laser planes according to their order includes: According to the order of the laser planes, the laser plane at the starting position is numbered as the starting number, and the laser plane at the ending position is numbered as the ending number.

5. The three-dimensional data acquisition method according to claim 4, characterized in that, The step of filtering the candidate 3D point set according to the number of the laser plane includes: Using the starting point number and the ending point number as the critical interval, traverse the relationship between each candidate 3D point in the candidate 3D point set and the critical interval; When a candidate 3D point falls into the critical interval, the candidate 3D point is retained. If a candidate 3D point does not fall within the critical interval, then the candidate 3D point is eliminated.

6. The three-dimensional data acquisition method according to any one of claims 1 to 5, characterized in that, The step of performing limit constraint matching on candidate laser segments in at least two synchronized two-dimensional images to obtain candidate segment pairs includes: Obtain the internal and external parameters of the 3D scanning device; Based on the aforementioned intrinsic and extrinsic parameters, limit constraint matching is performed between candidate laser segments in at least two synchronized two-dimensional images according to the principle of limit constraints to obtain corresponding candidate segment pairs.

7. The three-dimensional data acquisition method according to claim 6, characterized in that, Based on the intrinsic and extrinsic parameters, the process of matching candidate laser segments in at least two synchronized two-dimensional images according to the principle of limit constraints to obtain corresponding candidate segment pairs includes: Two-dimensional feature points are extracted from candidate laser segments in at least two synchronized two-dimensional images, respectively; Based on the aforementioned intrinsic and extrinsic parameters, limit constraint matching is performed between the two-dimensional feature points corresponding to the two-dimensional image according to the principle of limit constraint to obtain matching point pairs. Based on the matching point pairs, the corresponding candidate segment pairs are determined.

8. The three-dimensional data acquisition method according to claim 6, characterized in that, The process of determining candidate laser segments in each of the two-dimensional images includes: Feature recognition is performed on the two-dimensional image to obtain laser feature segments; Based on the laser plane, the laser feature segments are screened to obtain candidate laser segments.

9. A three-dimensional data acquisition device, applied in a three-dimensional scanning device, characterized in that, The device includes: an acquisition module, a matching module, a reconstruction module, and an evaluation module; The acquisition module is used to acquire a laser plane and at least two synchronized two-dimensional images based on a three-dimensional scanning device, and to determine candidate laser segments in each of the two-dimensional images; each of the two-dimensional images corresponds to an image acquisition device of the three-dimensional scanning device; The matching module is used to perform limit constraint matching on candidate laser segments in at least two of the two-dimensional images to obtain candidate segment pairs; The reconstruction module is used to perform three-dimensional reconstruction on each pair of candidate fragments to obtain the corresponding candidate three-dimensional point set; The evaluation module is used to evaluate the distance of the candidate 3D point set based on the laser plane, and determine the target 3D point set based on the evaluation result, comprising: Determine the distance from each candidate 3D point in the candidate 3D point set to each laser plane, and use the minimum distance as the screening criterion to determine the real laser plane corresponding to the candidate 3D point; When the distance between the candidate 3D point and the real laser plane satisfies a preset threshold, the candidate 3D point is determined as the target 3D point set.

10. A three-dimensional scanning device, characterized in that, It includes at least two image acquisition devices that are set up in sync, at least one laser projector, and a processor; The at least one laser projector is used to simultaneously project at least two laser lines onto the object to be scanned. The at least two image acquisition devices are connected to the processor and are used to synchronously acquire two-dimensional images of the object to be scanned. The processor is used to execute the steps of the three-dimensional data acquisition method according to any one of claims 1 to 8.

11. A computer device, comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the steps of the three-dimensional data acquisition method according to any one of claims 1 to 8.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the three-dimensional data acquisition method according to any one of claims 1 to 8.

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