Method for rigid multi-geometry outer surface reconstruction based on rotational imaging

Abstract

The application discloses a rigid multi-geometry outer surface reconstruction method based on rotation imaging and belongs to the technical field of three-dimensional reconstruction. The method comprises the following steps: center of rotation calibration; collecting three-dimensional point cloud data of the surface of an object to be reconstructed; performing pretreatment work such as removing stray points and repairing holes on the collected three-dimensional point cloud data; reconstructing a point cloud model of a measured object by using a surface reconstruction algorithm based on the pretreated three-dimensional point cloud data; and performing mesh optimization on the constructed point cloud model to complete outer surface reconstruction. The application supports an arbitrary internal rotation axis and is suitable for complex geometrical shapes such as annular and prism. A self-calibration method based on a standard part reduces the influence of equipment installation errors. A dynamic algorithm optimizes point cloud reconstruction and can realize micron-level three-dimensional reconstruction requirements.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of three-dimensional reconstruction, and particularly relates to a rigid multi-geometric surface reconstruction method based on rotary imaging. BACKGROUND

[0002] Traditional three-dimensional reconstruction methods usually rely on multi-view image stitching or laser scanning, and have problems such as high device cost and difficulty in eliminating blind areas. For rigid objects with rotational symmetry, the existing technology usually requires the rotational axis to coincide with the geometric center, which limits the adaptability of complex-shaped objects, and in addition, insufficient calibration accuracy also easily leads to reconstruction error accumulation. SUMMARY

[0003] The purpose of the embodiment of the application is to provide a rigid multi-geometric surface reconstruction method based on rotary imaging, which supports an arbitrary internal rotation axis and adapts to complex geometric shapes such as annular and prismatic bodies; a self-calibration method based on standard parts reduces the influence of device installation errors; a dynamic algorithm optimizes point cloud reconstruction, and can realize micron-level three-dimensional reconstruction requirements, thereby at least one of the technical problems involved in the background technology can be solved.

[0004] In order to solve the above technical problems, the application is implemented as follows:

[0005] The embodiment of the application provides a rigid multi-geometric surface reconstruction method based on rotary imaging, comprising the following steps:

[0006] Step S1, rotation center calibration, specifically comprising the following steps:

[0007] Step S11, a high-precision cylindrical reference part is installed radially on the object carrying turntable, and the axis of the cylindrical reference part is arranged in line with the rotation center of the object carrying turntable;

[0008] Step S12, a line laser three-dimensional measurement system comprising a three-dimensional camera and a line laser sensor is configured, the light plane of the line laser three-dimensional measurement system is orthogonal to the axial direction of the object carrying turntable, a space orthogonal measurement coordinate system is constructed, the X-axis direction of the space orthogonal measurement coordinate system represents the laser line width direction, the Y-axis direction represents the light plane stretching direction, and the Z direction represents the depth direction;

[0009] Step S13, a high-precision linear motion module is carried on the object carrying turntable, and the motion direction is orthogonal to the light plane;

[0010] Step S14, the object carrying turntable is controlled to move along the Y-axis direction at a constant speed v, and the line laser sensor is triggered synchronously to perform multiple high-speed sampling, and point cloud data of the surface of the cylindrical reference part is acquired;

[0011] Step S15: Based on the geometric features of the cylindrical reference component, when the rotating stage moves to make the rotation center coincide with the optical plane, the laser measurement point cloud exhibits the maximum depth value on the surface of the cylindrical reference component. The depth extreme value D is determined by calculating the axial depth distribution of the point cloud in each frame in real time. max Point cloud frame Its corresponding spatial position is the radial projection of the rotation center;

[0012] Step S16, let the minimum Y-axis coordinate acquired by the initial time-line laser three-dimensional measurement system be Y. min Depth extremum D max Point cloud frame The Y-axis coordinate is Y max The motion time parameter of the turntable is: t = (Y max - Y min ) / v; By moving backward at velocity v for time t from the initial position, the light plane is spatially aligned with the rotation center at a submicron level, thus completing the rotation center calibration;

[0013] Step S2: Drive the object to be reconstructed or the 3D camera to rotate at least 360° around the rotation center, and collect 3D point cloud data of the surface of the object to be reconstructed once every time it rotates by a preset fixed angle.

[0014] Step S3 involves preprocessing the acquired 3D point cloud data, such as removing noise and repairing holes, to remove outliers and point clouds of objects not to be reconstructed, thus ensuring the integrity of the point cloud.

[0015] Step S4: Based on the preprocessed 3D point cloud data, the point cloud model of the object under test is reconstructed using a surface reconstruction algorithm.

[0016] Step S5: The point cloud model is meshed using the BPA meshing algorithm. At the same time, the triangular mesh is subdivided using the isotropic explicit remeshing method to achieve uniform mesh distribution. For small protrusions on the mesh surface, the Taubin smoothing algorithm is used to smooth the mesh surface to ensure the quality of the mesh model and achieve accurate and complete reconstruction of the outer surface.

[0017] Optionally, in step S14, the high-speed sampling frequency is ≥1 kHz.

[0018] Optionally, in step S15, the extreme value of the axial depth is represented by the following formula:

[0019] D max = PCD i [ max{‖P jz ‖ | P jz ∈ PCD i}], j = 1, 2,..., N ;

[0020] in P jz This represents the depth of any point in the point cloud. D max This represents a point with the greatest depth in the point cloud, and the frame containing that point is the depth extreme value frame. To photograph a point cloud model.

[0021] Optionally, in step S4, a surface reconstruction algorithm is used to reconstruct the point cloud model of the object under test, specifically including:

[0022] Set a specific rotation angle θ for all points in the 3D point cloud data, assuming the rotation direction is... y The rotation angle is determined by the axial direction. θ Calculated by the following formula:

[0023] ;

[0024] In the formula, Indicates the spatial resolution of the 3D camera in the direction of motion; Represents a point on a point cloud coordinate; Indicates the point cloud at the moment the rotation begins. value; Indicates the shooting frequency;

[0025] Based on the calculated rotation angle θ The dynamic rotation matrix R can be obtained from the following formula:

[0026] ;

[0027] In the formula, The unit vector representing the axis of rotation can be easily solved in the camera coordinate system after the rotation center is calibrated in step S16. Representing vectors The outer product matrix; express The cross product matrix; This represents a 3x3 identity matrix;

[0028] Photographing point clouds Converted into a spatial 3D point cloud of the corresponding object model To achieve the goal of capturing point cloud models To the actual point cloud model The conversion completes the reconstruction of the point cloud on the surface of the measured object, resulting in a three-dimensional point cloud in space. It is derived from the following formula:

[0029] ;

[0030] In the formula, δ represents a coordinate proportion conversion factor.

[0031] Compared with the prior art, the present application has the following beneficial effects:

[0032] (1) Only the rotation center needs to be calibrated, and no preset geometric model or geometric center of the measured object is needed, and the reconstruction is applicable to any rigid polyhedron, which significantly improves the applicability.

[0033] (2) The self-calibration method based on the standard part controls the calibration accuracy to the sub-micron level, and reduces the influence of equipment installation errors.

[0034] (3) In the dynamic algorithm optimization point cloud reconstruction, the point-by-point processing method well preserves the original characteristics of the measured object, especially for complex polyhedrons that are difficult to handle in traditional methods, the present application can realize the three-dimensional reconstruction requirement of the whole surface to the micron level.

[0035] (4) The overall operation process is simplified, especially after the rotation center is calibrated, the point cloud can be directly reconstructed, which greatly improves the practicability and efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0037] Figure 1 The flow chart of the rigid multi-geometric body outer surface reconstruction method based on rotation imaging provided by the present application;

[0038] Figure 2 is one of the hardware structure schematic diagrams of the electronic device provided by the present application;

[0039] Figure 3 is the second hardware structure schematic diagram of the electronic device provided by the present application. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0041] The terms "first," "second," etc., used in the specification and claims of this invention are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0042] Please see Figure 1 As shown, this embodiment of the invention provides a method for reconstructing the outer surface of a rigid multi-geometry exterior based on rotational imaging, comprising the following steps:

[0043] Step S1, rotation center calibration, specifically includes the following steps:

[0044] Step S11: A high-precision cylindrical reference component is radially installed on the turntable, with the axis of the cylindrical reference component being collinear with the rotation center of the turntable.

[0045] Step S12: Configure a line laser 3D measurement system including a 3D camera and a line laser sensor. Its light plane is orthogonal to the axis of the rotating platform. Construct a spatial rectangular measurement coordinate system. The X-axis of this coordinate system represents the laser line width direction, the Y-axis represents the light plane extension direction, and the Z-axis represents the depth direction.

[0046] Step S13: A high-precision linear motion module is mounted on the turntable, with the motion direction orthogonal to the light plane;

[0047] Step S14: Control the turntable to move along the Y-axis at a constant speed v, and synchronously trigger the line laser sensor to perform multiple high-speed samplings to acquire point cloud data of the cylindrical reference part surface.

[0048] Step S15: Based on the geometric features of the cylindrical reference component, when the rotating stage moves to make the rotation center coincide with the optical plane, the laser measurement point cloud exhibits the maximum depth value on the surface of the cylindrical reference component. The depth extreme value D is determined by calculating the axial depth distribution of the point cloud in each frame in real time. max Point cloud frame Its corresponding spatial position is the radial projection of the rotation center;

[0049] Step S16, let the minimum Y-axis coordinate acquired by the initial time-line laser three-dimensional measurement system be Y. min Depth extremum D max Point cloud frame The Y-axis coordinate is Y max, the motion time parameter of the carrier turntable is: t = (Y max - Y min ) / v; by reversing the motion at the initial position with a speed v for a time t, the light plane is aligned with the rotation center at a sub-micron level, and the rotation center calibration is completed;

[0050] Step S2, drive the object to be reconstructed or the three-dimensional camera to rotate around the rotation center by at least 360°, and collect three-dimensional point cloud data of the surface of the object to be reconstructed once every predetermined fixed angle;

[0051] Step S3, pre-processing work such as outlier removal and hole repair is performed on the collected three-dimensional point cloud data, outliers and non-object-to-be-reconstructed point clouds are removed, and the integrity of the point cloud is ensured;

[0052] Step S4, based on the pre-processed three-dimensional point cloud data, a surface reconstruction algorithm is used to reconstruct the measured object point cloud model;

[0053] Step S5, the BPA gridding algorithm is used to complete the gridding of the point cloud model, and the isotropic explicit remeshing method is used to subdivide the triangular mesh to realize uniform distribution of the mesh; for small protrusions on the mesh surface, taubin smoothing algorithm is used for smoothing the mesh surface to ensure the quality of the mesh model and realize accurate and complete reconstruction of the outer surface.

[0054] In step S14, the high-speed sampling frequency is ≥1 kHz.

[0055] In step S15, the axial depth extreme value is represented by the following formula:

[0056] D max = PCD i [ max{‖P jz ‖ | P jz ∈ PCD i }], j = 1, 2,..., N ;

[0057] wherein P jz represents the depth of any one point in the point cloud; D max represents a point with the largest depth in the point cloud, and the frame in which it is located is the depth extreme value frame; is a shot point cloud model.

[0058] In step S4, the surface reconstruction algorithm is used to reconstruct the measured object point cloud model, which specifically includes:

[0059] A specific rotation angle θ is set for all points in the three-dimensional point cloud data, and the rotation direction isy axis direction, the rotation angle θ is calculated by the following formula:

[0060] ;

[0061] In the formula, represents the spatial resolution of the three-dimensional camera in the motion direction; represents the coordinates of a certain point on the point cloud; ; represents the value of the point cloud at the start time of rotation; ; represents the shooting frequency;

[0062] Based on the calculated rotation angle θ , the dynamic rotation matrix R can be obtained by the following formula:

[0063] ;

[0064] In the formula, represents the unit vector of the rotation axis, which can be easily solved in the camera coordinate system after the rotation center is calibrated in step S16; represents the outer product matrix of the vector ; represents the cross product matrix of ; represents a three-row three-column unit matrix, and the diagonal elements are 1 and the rest are 0.

[0065] The photographed point cloud is converted into a spatial three-dimensional point cloud corresponding to the object model, realizing the conversion from the photographed point cloud model to the actual point cloud model , completing the surface point cloud reconstruction of the measured object, and the spatial three-dimensional point cloud is converted by the following formula:

[0066] ;

[0067] In the formula, δ represents the coordinate scaling conversion factor.

[0068] Based on the above point cloud reconstruction algorithm, each point cloud point will perform a rotation operation, realizing the conversion from the photographed point cloud to the actual point cloud , and finally completing the high-precision reconstruction of the object surface.

[0069] As Figure 2As shown, the embodiment of the present application further provides an electronic device 600, which comprises a processor 601, a memory 602, a program or instruction stored in the memory 602 and executable on the processor 601, the program or instruction is executed by the processor 601 to realize the processes of the above-mentioned rigid multi-geometry outer surface reconstruction method based on rotation imaging and achieve the same technical effects, to avoid repetition, which will not be described here.

[0070] It should be noted that the first electronic device in the embodiment of the present application includes the mobile electronic device and the non-mobile electronic device.

[0071] Figure 3 The hardware structure schematic diagram of an electronic device for realizing the embodiment of the present application.

[0072] The electronic device 700 includes but is not limited to: a radio frequency unit 701, a network module 702, an audio output unit 703, an input unit 704, a sensor 705, a display unit 706, a user input unit 707, an interface unit 708, a memory 709, and a processor 710, etc.

[0073] Those skilled in the art can understand that the electronic device 700 can further include a power supply (such as a battery) for supplying power to each component, and the power supply can be logically connected to the processor 710 through a power management system, so as to realize the functions of managing charging, discharging, and power consumption management through the power management system. Figure 3 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and the electronic device can include more or less components than the figure, or combine certain components, or different component arrangements, which will not be described here.

[0074] It should be understood that in the embodiments of the present application, the input unit 704 can include a graphics processing unit (GPU) 7041 and a microphone 7042, and the graphics processing unit 7041 processes image data of a still image or a video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 706 can include a display panel 7061, which can be configured in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit 707 includes a touch panel 7071 and other input devices 7072. The touch panel 7071 is also called a touch screen. The touch panel 7071 can include two parts of a touch detection device and a touch controller. The other input devices 7072 can include, but are not limited to, a physical keyboard, function keys (such as volume control keys, on-off keys, etc.), trackballs, mice, joysticks, etc., which will not be described here. The memory 709 can be used to store software programs and various data, including but not limited to application programs and operating systems. The processor 710 can integrate an application processor and a modem processor, wherein the application processor mainly processes operating systems, user interfaces and application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 710.

[0075] The embodiments of the present application also provide a readable storage medium, which stores programs or instructions, and the programs or instructions are executed by a processor to realize the processes of the above-mentioned rigid multi-geometry outer surface reconstruction method based on rotation imaging, and achieve the same technical effects. To avoid repetition, details will not be described here.

[0076] The processor is the processor in the electronic device described in the above-mentioned embodiments. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0077] The embodiments of the present application further provide a chip, which includes a processor and a communication interface, the communication interface is coupled with the processor, and the processor is used to run programs or instructions to realize the processes of the above-mentioned rigid multi-geometry outer surface reconstruction method based on rotation imaging, and achieve the same technical effects. To avoid repetition, details will not be described here.

[0078] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip, etc.

[0079] It should be noted that, in this document, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element. Furthermore, it should be noted that the methods and apparatuses of the present embodiments are not limited by the order of the steps or the sequence for performing the steps, as some steps can occur simultaneously, in other steps can occur sequentially, or in between other steps can occur at least partially concurrently, unless expressly limited by the context of the steps. Also, features described with respect to certain examples can be combined in other examples.

[0080] Those skilled in the art can clearly understand that the above-mentioned embodiment method can be realized by means of software and necessary general hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of software product, and the computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a plurality of instructions for making a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) execute the method described in each embodiment of the present application.

[0081] The embodiments of the present application are described above in conjunction with the drawings, but the present application is not limited to the above-mentioned specific embodiments, and the above-mentioned specific embodiments are only illustrative, not restrictive, and those skilled in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims, which all belong to the protection of the present application.

Claims

1. A method for reconstructing the outer surface of a rigid multi-geometry exterior based on rotational imaging, characterized in that, Includes the following steps: Step S1, rotation center calibration, specifically includes the following steps: Step S11: A high-precision cylindrical reference component is radially installed on the turntable, with the axis of the cylindrical reference component being collinear with the rotation center of the turntable. Step S12: Configure a line laser 3D measurement system including a 3D camera and a line laser sensor. Its light plane is orthogonal to the rotation center of the turntable. Construct a spatial rectangular measurement coordinate system. The X-axis of this coordinate system represents the laser line width direction, the Y-axis represents the light plane extension direction, and the Z-axis represents the depth direction. Step S13: A high-precision linear motion module is mounted on the turntable, with the motion direction orthogonal to the light plane; Step S14: Control the turntable to move along the Y-axis at a constant speed v, and synchronously trigger the line laser sensor to perform multiple high-speed samplings to acquire point cloud data of the cylindrical reference part surface. Step S15: Based on the geometric features of the cylindrical reference component, when the rotating stage moves to make the rotation center coincide with the optical plane, the laser measurement point cloud exhibits the maximum depth value on the surface of the cylindrical reference component. The depth extreme value D is determined by calculating the axial depth distribution of the point cloud in each frame in real time. max Point cloud frame Its corresponding spatial position is the radial projection of the rotation center; Step S16, let the minimum Y-axis coordinate acquired by the initial time-line laser three-dimensional measurement system be Y. min Depth extremum D max Point cloud frame The Y-axis coordinate is Y max Then the motion time t of the rotating platform is t = (Y max - Y min ) / v, where the alignment of the optical plane with the rotation center is achieved by controlling the rotating platform to move backward at a speed v for a time t from its initial position. The alignment accuracy can reach the submicron level, thus completing the rotation center calibration. Step S2: Drive the object to be reconstructed or the 3D camera to rotate at least 360° around the rotation center, and collect 3D point cloud data of the surface of the object to be reconstructed once every time it rotates by a preset fixed angle. Step S3: Preprocess the acquired 3D point cloud data to remove outliers and point clouds of objects not to be reconstructed. Step S4: After point cloud preprocessing, a surface reconstruction algorithm is used to restore the captured point cloud into the actual point cloud model of the object under test. Step S5: The point cloud model is meshed using the BPA meshing algorithm. At the same time, the triangular mesh is subdivided using the isotropic explicit remeshing method to achieve uniform mesh distribution. For small protrusions on the mesh surface, the Taubin smoothing algorithm is used to smooth the mesh surface to ensure the quality of the mesh model and achieve accurate and complete reconstruction of the outer surface.

2. The method according to claim 1, characterized in that, In step S14, the high-speed sampling frequency is ≥1 kHz.

3. The method according to claim 2, characterized in that, In step S15, the extreme value of axial depth is expressed by the following formula: D max =PCD i [ max{‖P jz ‖ | P jz ∈ PCD i }], j=1,2,...,N ； in P jz This represents the depth of any point in the point cloud. D max This represents a point with the greatest depth in the point cloud, and the frame containing that point is the depth extreme value frame. To photograph a point cloud model.

4. The method according to claim 3, characterized in that, In step S4, a surface reconstruction algorithm is used to reconstruct the point cloud model of the object under test, specifically including: Set a specific rotation angle θ for all points in the 3D point cloud data, assuming the rotation direction is... y The axial direction indicates the rotation angle. θ Calculated by the following formula: ； In the formula, Indicates the spatial resolution of the 3D camera in the direction of motion; Represents a point on a point cloud coordinate; Indicates the point cloud at the moment the rotation begins. value; Indicates the shooting frequency; Based on the calculated rotation angle θ The dynamic rotation matrix R can be obtained from the following formula: ； In the formula, The unit vector representing the axis of rotation; Representing vectors The outer product matrix; express The cross product matrix; This represents a 3x3 identity matrix; Photographing point clouds Converted into a spatial 3D point cloud of the corresponding object model To achieve the goal of capturing point cloud models To the actual point cloud model The conversion completes the reconstruction of the point cloud on the surface of the measured object, resulting in a three-dimensional point cloud in space. It is derived from the following formula: ； In the formula, δ represents the coordinate scaling factor.

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