CN115760549A - Processing method for flattening 3D data of curved surface - Google Patents
Processing method for flattening 3D data of curved surface Download PDFInfo
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- CN115760549A CN115760549A CN202211092434.XA CN202211092434A CN115760549A CN 115760549 A CN115760549 A CN 115760549A CN 202211092434 A CN202211092434 A CN 202211092434A CN 115760549 A CN115760549 A CN 115760549A
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Abstract
The invention discloses a processing method for flattening curved surface 3D data, which comprises the following steps: s1, scanning an object to be processed by 360 degrees to obtain 3D point cloud data (X, Y, Z); s2, listing the Z values of the 3D point cloud data (X, Y, Z) in the S1 by taking X and Y as the length and width of the image; s3, traversing the Z value of each row, solving the mean value of each row, respectively subtracting the mean value of the current row Z from the Z component of the current row in the 3D point cloud data (X, Y, Z), and sequentially using the Z component as the gray value of the current X and Y position in the corresponding image; s4, calculating the minimum value and the maximum value in the current column of the image, and normalizing the current column to be 0-255 according to the maximum value and the minimum value; and S5, converting the obtained 3D data with the height information into a planar single-channel gray-scale image. The invention converts the curved surface 3D data into 2D plane image, and uses the plane 2D image processing method to process the data.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a processing method for flattening curved surface 3D data.
Background
Aiming at monochromatic low-contrast entities such as rubber, tires and other production products, a contourgraph, a laser radar and other equipment are adopted to scan the entities for 360 degrees to obtain 3D point cloud data, but due to the fact that the data volume is huge, the requirement configuration on hardware is high in processing in a 3D mode, the corresponding cost is high, the consumed resources and the processing time are long, the real-time requirement of processing is difficult to meet, a measured object with height data in a 3D curved surface scene is difficult to detect and divide, if 2D equipment is adopted for shooting, the entities with small height difference and low monochromatic contrast are difficult to acquire. Therefore, the invention provides a processing method for flattening curved 3D data.
Disclosure of Invention
According to the processing method for flattening the curved surface 3D data, the curved surface 3D data is converted into the 2D plane image, the data is subjected to subsequent processing by using the plane 2D image processing method, and the data which is difficult to process under the curved surface 3D data is processed under the plane 2D mode by using the image algorithm.
In order to achieve the purpose, the invention adopts the following technical scheme:
a processing method for flattening curved surface 3D data comprises the following steps:
s1, scanning an object to be processed for 360 degrees to obtain 3D point cloud data (X, Y and Z);
s2, listing the Z values of the 3D point cloud data (X, Y, Z) in the S1 by taking X and Y as the length and width of the image;
s3, traversing the Z value of each row, solving the mean value of each row, respectively subtracting the mean value of the current row Z from the Z component of the current row in the 3D point cloud data (X, Y, Z), and sequentially using the Z component as the gray value of the current X and Y position in the corresponding image;
s4, calculating the minimum value and the maximum value in the current column of the image, and normalizing the current column to be 0-255 according to the maximum value and the minimum value;
and S5, converting the obtained 3D data with the height information into a planar single-channel gray-scale image.
Preferably, a single-channel gray-scale image of S5 is obtained, and the gray-scale image is used for subsequent target detection and segmentation operations.
Preferably, a profile instrument and a laser radar device are adopted to scan an object to be processed for 360 degrees, and 3D point cloud data are obtained.
Preferably, each row of the Z-value list processed in S2 has the same value.
Compared with the prior art, the processing method for flattening the curved surface 3D data greatly reduces the dependence on hardware, thereby reducing the cost. The complexity of algorithms such as target detection, identification and the like is high and immature under the 3D data, the 2D planarization treatment is easy to realize when the algorithms are switched to, the time consumption of the 2D data processing is short, and the algorithms can be processed in time.
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FIG. 1 is a flow chart of the process of the present invention.
Fig. 2 is a photograph before processing a three-dimensional view of a tire.
Fig. 3 is a processing diagram of fig. 2 after converting 3D into 2D.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments.
Referring to fig. 1, the processing method for flattening 3D data with a curved surface according to the present invention includes the following steps:
s1, scanning an object to be processed by 360 degrees to obtain 3D point cloud data (X, Y, Z);
s2, listing the Z values of the 3D point cloud data (X, Y, Z) in the S1 by taking X and Y as the length and width of the image;
s3, traversing the Z value of each row, solving the mean value of each row, respectively subtracting the mean value of the current row Z from the Z component of the current row in the 3D point cloud data (X, Y, Z), and sequentially using the Z component as the gray value of the current X and Y position in the corresponding image;
s4, calculating the minimum value and the maximum value in the current column of the image, and normalizing the current column to 0-255 according to the maximum value and the minimum value;
and S5, converting the obtained 3D data with the height information into a planar single-channel gray-scale image, and performing subsequent target detection and segmentation operation by using the gray-scale image.
As shown in table 2, each row of the Z-value list obtained after processing in S2 has the same value.
As shown in fig. 2 and 3, fig. 2 is a photograph before the tire three-dimensional map processing. Fig. 3 is a processing diagram of fig. 2 after converting 3D to 2D. The processed picture can be subjected to subsequent target detection and segmentation operations.
As shown in table 1, pre-treatment data;
as shown in table 2, the processed data;
so that the curved surface data can be flattened into the planar data.
The hardware equipment is not limited to the contourgraph and the laser radar and comprises all three-dimensional data acquisition equipment; the two-dimensional data after the algorithm conversion is not limited to the image, and includes all two-dimensional data. The application scene of the invention is not limited to a fixed scene, and the invention is used in all application scenes using three-dimensional acquisition equipment.
In the invention, the average value of the current column can also be 1 column on the left and right of the current column, namely a three-column average value; 2 columns left and right, namely 5 columns of mean values; and so on. The column average value can also be a method of taking a minimum value, a median, a maximum value, a fitting straight line and the like; the normalization by columns is not limited to one column, and may be multiple columns. The images after difference making are not limited to maximum and minimum normalization, and can also be processed by sigmoid and the like. The normalization range is not limited to 0-255, and can be within a specified value range according to actual requirements.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (5)
1. A processing method for flattening curved surface 3D data is characterized by comprising the following steps: the method comprises the following steps:
s1, scanning an object to be processed by 360 degrees to obtain 3D point cloud data (X, Y, Z);
s2, listing the Z values of the 3D point cloud data (X, Y, Z) in the S1 by taking X and Y as the length and width of the image;
s3, traversing the Z value of each row, solving the mean value of each row, respectively subtracting the mean value of the current row Z from the Z component of the current row in the 3D point cloud data (X, Y, Z), and sequentially using the Z component as the gray value of the current X and Y position in the corresponding image;
s4, calculating the minimum value and the maximum value in the current column of the image, and normalizing the current column to be 0-255 according to the maximum value and the minimum value;
and S5, converting the obtained 3D data with the height information into a planar single-channel gray-scale image.
2. The method as claimed in claim 1, wherein a single-channel gray-scale image of S5 with obvious gray-scale difference and texture is obtained, and the gray-scale image is used for subsequent object detection and segmentation.
3. The processing method for flattening the 3D data with the curved surface according to claim 1, characterized in that a profiler and a laser radar device are adopted to scan an object to be processed for 360 degrees to obtain the 3D point cloud data.
4. The method as claimed in claim 1, wherein the Z value obtained by the processing in S3 is a decimal value having both positive and negative values around 0, and the precision is high.
5. The method as claimed in claim 1, wherein the Z value obtained after processing in S4 is enlarged in the digital domain space, and has no negative value, and the image contrast is increased, and the details are clearer.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116503386A (en) * | 2023-06-25 | 2023-07-28 | 宁德时代新能源科技股份有限公司 | Method and device for detecting structural adhesive, terminal and computer readable storage medium |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116503386A (en) * | 2023-06-25 | 2023-07-28 | 宁德时代新能源科技股份有限公司 | Method and device for detecting structural adhesive, terminal and computer readable storage medium |
| CN116503386B (en) * | 2023-06-25 | 2023-12-01 | 宁德时代新能源科技股份有限公司 | Method and device for detecting structural adhesive, terminal and computer readable storage medium |
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