CN111968223A - Three-dimensional reconstruction system for 3D printing process based on machine vision - Google Patents
Three-dimensional reconstruction system for 3D printing process based on machine vision Download PDFInfo
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- CN111968223A CN111968223A CN202010796821.6A CN202010796821A CN111968223A CN 111968223 A CN111968223 A CN 111968223A CN 202010796821 A CN202010796821 A CN 202010796821A CN 111968223 A CN111968223 A CN 111968223A
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- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000010146 3D printing Methods 0.000 title claims abstract description 18
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- 238000003384 imaging method Methods 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 4
- 238000011084 recovery Methods 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 3
- 239000013589 supplement Substances 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 description 5
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- 230000009286 beneficial effect Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/80—Geometric correction
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
- G06T2200/04—Indexing scheme for image data processing or generation, in general involving 3D image data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
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Abstract
The invention discloses a three-dimensional reconstruction system for a 3D printing process based on machine vision, and relates to the technical field of 3D printing; the system comprises an image acquisition module, a camera calibration module, a feature extraction module, a point cloud reconstruction module and a surface reconstruction module; the device comprises an image acquisition module, a camera calibration module, a feature extraction module, a point cloud reconstruction module and a surface reconstruction module, wherein the image acquisition module is connected with the camera calibration module, acquires images through a camera and realizes multi-point calibration through the camera calibration module; the invention is convenient for realizing the rapid acquisition and extraction of images, can realize the point cloud and surface reconstruction and is convenient to use; the accuracy can be improved, the operation is rapid and stable, and the time is saved during the operation.
Description
Technical Field
The invention belongs to the technical field of 3D printing, and particularly relates to a three-dimensional reconstruction system for a 3D printing process based on machine vision.
Background
3D printing (3 DP), one of the rapid prototyping technologies, is a technology that constructs an object by printing layer by layer using an adhesive material such as powdered metal or plastic based on a digital model file. 3D printing is typically achieved using digital technology material printers. The method is often used for manufacturing models in the fields of mold manufacturing, industrial design and the like, and is gradually used for directly manufacturing some products, and parts printed by the technology are already available. The technology has applications in jewelry, footwear, industrial design, construction, engineering and construction (AEC), automotive, aerospace, dental and medical industries, education, geographic information systems, civil engineering, firearms, and other fields.
The existing three-dimensional reconstruction system for the 3D printing process has the defects of error of printed articles, complex operation and long reconstruction time due to the accuracy during reconstruction.
Disclosure of Invention
The problems that an error exists in a printed article, the operation is complex and the reconstruction time is long due to the accuracy of a three-dimensional reconstruction system in the existing 3D printing process during reconstruction are solved; the invention aims to provide a three-dimensional reconstruction system for a 3D printing process based on machine vision.
The invention discloses a three-dimensional reconstruction system for a 3D printing process based on machine vision, which comprises an image acquisition module, a camera calibration module, a feature extraction module, a point cloud reconstruction module and a surface reconstruction module, wherein the image acquisition module is used for acquiring images of a plurality of images; the image acquisition module is connected with the camera calibration module, the image acquisition module acquires images through a camera, multi-point calibration is realized through the camera calibration module, the camera calibration module is connected with the feature extraction module, the feature extraction module extracts image features, the feature extraction module is connected with the point cloud reconstruction module, the point cloud reconstruction module is connected with the surface reconstruction module,
preferably, the image acquisition module is a camera imaging model, is determined by the relationship between each point on the image and the corresponding point in the real space, completes camera calibration through multiple points, determines the internal and external parameters of the camera, and establishes the model.
Preferably, the image acquisition module comprises a binocular camera, a light supplement lamp, a support column and a rotating motor; the binocular camera is installed in the upper end of support column, and the light filling lamp is installed in the two camera outsides of binocular camera, and the bottom of support column is connected with the hub connection of rotating electrical machines, is provided with the enhancement strip on the lateral wall of support column.
Preferably, the camera calibration module is configured to complete calibration of the camera by solving the projection matrix at multiple points, and correct distortion by using the obtained parameters.
Preferably, the point cloud reconstruction module reconstructs the point cloud by adopting a motion recovery structure, reconstructs the point cloud by adopting multi-view stereoscopic vision dense reconstruction, and finally constructs a grid from the point cloud by using a surface reconstruction method.
Preferably, the point cloud reconstruction module performs a SIFT algorithm and an SFM algorithm when reconstructing the sparse point cloud.
Compared with the prior art, the invention has the beneficial effects that:
the method is convenient to realize rapid acquisition and extraction of images, can realize point cloud and surface reconstruction, and is convenient to use;
secondly, the accuracy can be improved, the operation is rapid and stable, and the time is saved during the operation.
Drawings
For ease of illustration, the invention is described in detail by the following detailed description and the accompanying drawings.
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a schematic structural diagram of an image acquisition module according to the present invention;
fig. 3 is a schematic structural view of the support column of the present invention.
1-a binocular camera; 2, a light supplement lamp; 3-a support column; 4-a rotating electrical machine; 31-reinforcing bars.
Detailed Description
In order that the objects, aspects and advantages of the invention will become more apparent, the invention will be described by way of example only, and in connection with the accompanying drawings. It is to be understood that such description is merely illustrative and not intended to limit the scope of the present invention. The structure, proportion, size and the like shown in the drawings are only used for matching with the content disclosed in the specification, so that the person skilled in the art can understand and read the description, and the description is not used for limiting the limit condition of the implementation of the invention, so the method has no technical essence, and any structural modification, proportion relation change or size adjustment still falls within the range covered by the technical content disclosed by the invention without affecting the effect and the achievable purpose of the invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
It should be noted that, in order to avoid obscuring the present invention with unnecessary details, only the structures and/or processing steps closely related to the scheme according to the present invention are shown in the drawings, and other details not so relevant to the present invention are omitted.
As shown in fig. 1, the following technical solutions are adopted in the present embodiment: the system comprises an image acquisition module, a camera calibration module, a feature extraction module, a point cloud reconstruction module and a surface reconstruction module; the image acquisition module is connected with the camera calibration module, the image acquisition module acquires images through a camera, multi-point calibration is realized through the camera calibration module, the camera calibration module is connected with the feature extraction module, the feature extraction module extracts image features, the feature extraction module is connected with the point cloud reconstruction module, the point cloud reconstruction module is connected with the surface reconstruction module,
further, the image acquisition module is a camera imaging model, is determined by the relation between each point on the image and the corresponding point in the real space, completes camera calibration through multiple points, determines the internal and external parameters of the camera, and establishes the model.
As shown in fig. 2 and 3, further, the image acquisition module includes a binocular camera 1, a fill-in light 2, a support column 3, and a rotating motor 4; binocular camera 1 is installed in the upper end of support column 3, and light filling lamp 2 is installed in the two camera outsides of binocular camera 1, and the bottom of support column 3 is connected with the hub connection of rotating electrical machines 4, is provided with on the lateral wall of support column 3 and strengthens strip 31.
Further, the camera calibration module is used for completing calibration of the camera by solving a projection matrix through multiple points, and correcting distortion by using the solved parameters.
Further, the point cloud reconstruction module reconstructs the point cloud by adopting a motion recovery structure, reconstructs the point cloud by adopting multi-view stereoscopic vision dense reconstruction, and finally constructs a grid from the point cloud by using a surface reconstruction method.
Further, the point cloud reconstruction module performs a SIFT algorithm and an SFM algorithm when reconstructing the sparse point cloud.
The principle of the specific embodiment is as follows:
1. research on a camera projection model: the camera imaging model is determined by the relation between each point on the image and the corresponding point in the real space, the camera calibration is completed through multiple points, the internal and external parameters of the camera are determined, and the model is established.
2. Study of three-dimensional reconstruction; the method comprises the following steps: the method comprises the steps of image acquisition, camera calibration, feature extraction, point cloud reconstruction, surface reconstruction and three-dimensional model design reconstruction.
3. Aiming at the real-time requirement, a reconstruction improvement algorithm is provided, the feature extraction speed is increased, and the point cloud reconstruction process is accelerated.
The camera projection model comprises the steps of solving a projection matrix by multiple points to finish the calibration of the camera, and correcting distortion by using the obtained parameters.
And aiming at application characteristics, completing three-dimensional reconstruction process design, wherein a motion recovery structure is adopted to reconstruct point cloud, multi-view stereoscopic vision dense reconstruction is adopted, and finally a surface reconstruction method is used to construct a grid from the point cloud.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (6)
1. The utility model provides a three-dimensional system that rebuilds of 3D printing process based on machine vision which characterized in that: the system comprises an image acquisition module, a camera calibration module, a feature extraction module, a point cloud reconstruction module and a surface reconstruction module; the image acquisition module is connected with the camera calibration module, the image acquisition module acquires images through a camera, multi-point calibration is realized through the camera calibration module, the camera calibration module is connected with the feature extraction module, the feature extraction module extracts image features, the feature extraction module is connected with the point cloud reconstruction module, and the point cloud reconstruction module is connected with the surface reconstruction module.
2. The machine-vision-based 3D printing process three-dimensional reconstruction system of claim 1, wherein: the image acquisition module is a camera imaging model, is determined by the relation between each point on the image and the corresponding point in the real space, finishes camera calibration through multiple points, determines the internal and external parameters of the camera, and establishes a model.
3. The machine-vision-based 3D printing process three-dimensional reconstruction system of claim 1, wherein: the camera calibration module is used for completing calibration of the camera by solving a projection matrix through multiple points and correcting distortion by using the obtained parameters.
4. The machine-vision-based 3D printing process three-dimensional reconstruction system of claim 1, wherein: the point cloud reconstruction module reconstructs the point cloud by adopting a motion recovery structure, reconstructs the multi-view stereoscopic vision dense reconstruction and finally constructs a grid from the point cloud by using a surface reconstruction method.
5. The machine-vision-based 3D printing process three-dimensional reconstruction system of claim 1, wherein: and the point cloud reconstruction module is used for reconstructing the sparse point cloud through an SIFT algorithm and an SFM algorithm.
6. The machine-vision-based 3D printing process three-dimensional reconstruction system of claim 1, wherein: the image acquisition module comprises a binocular camera, a light supplement lamp, a support column and a rotating motor; the binocular camera is installed in the upper end of support column, and the light filling lamp is installed in the two camera outsides of binocular camera, and the bottom of support column is connected with the hub connection of rotating electrical machines, is provided with the enhancement strip on the lateral wall of support column.
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| CN202010796821.6A CN111968223A (en) | 2020-08-10 | 2020-08-10 | Three-dimensional reconstruction system for 3D printing process based on machine vision |
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| CN202010796821.6A CN111968223A (en) | 2020-08-10 | 2020-08-10 | Three-dimensional reconstruction system for 3D printing process based on machine vision |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113459660A (en) * | 2021-09-02 | 2021-10-01 | 深圳市创博未来科技有限公司 | Multi-surface spliced printed matter printing simulation system based on three-dimensional modeling |
| CN113601833A (en) * | 2021-08-04 | 2021-11-05 | 温州科技职业学院 | FDM three-dimensional printing control system |
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| CN108335350A (en) * | 2018-02-06 | 2018-07-27 | 聊城大学 | The three-dimensional rebuilding method of binocular stereo vision |
| CN110782521A (en) * | 2019-09-06 | 2020-02-11 | 重庆东渝中能实业有限公司 | Mobile terminal three-dimensional reconstruction and model restoration method and system |
| CN111080685A (en) * | 2019-12-17 | 2020-04-28 | 北京工业大学 | Airplane sheet metal part three-dimensional reconstruction method and system based on multi-view stereoscopic vision |
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2020
- 2020-08-10 CN CN202010796821.6A patent/CN111968223A/en active Pending
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| CN108335350A (en) * | 2018-02-06 | 2018-07-27 | 聊城大学 | The three-dimensional rebuilding method of binocular stereo vision |
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