Three-dimensional surface quality classification method, system and storage medium based on point cloud data
A technology of 3D surface and point cloud data, applied in image data processing, neural learning method, image analysis and other directions, can solve the problems of difficult realization of 3D surface, difficult to apply to large-scale point cloud data, etc., to achieve the effect of wide applicability
Active Publication Date: 2022-03-18
晶仁光电科技(苏州)有限公司
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However, this method requires the establishment of a mathematical model of the surface, which is difficult to implement for complex three-dimensional surfaces
Moreover, this method is also only suitable for data processing of three-coordinate measurement, and it is difficult to apply to large-scale point cloud data
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[0054] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be pointed out that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.
[0055] see figure 1 , a three-dimensional surface quality classification method based on point cloud data, including the following steps:
[0056] Step 1: Measure the three-dimensional curved surface with high-definition measuring equipment, obtain three-dimensional point cloud data, and obtain the coordinate value P(x, y, z) of each measurement point.
[0057] Step 2: Process the data, and eliminate outliers in the point cloud data generated by the measurement. For each measurement point,...
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The invention provides a method, system and storage medium for classifying the quality of three-dimensional curved surfaces based on point cloud data, including: measuring the three-dimensional curved surface, obtaining three-dimensional coordinate data of measurement points; Carry out registration; divide the 3D surface into several sub-regions according to the geometric characteristics of the measurement points, and automatically extract the sub-region features through the neural network according to the 3D surface segmentation results; connect the sub-region features and process them through the classification network to obtain 3D Surface quality classification results. The invention can be applied to three-dimensional curved surfaces of any shape, can effectively evaluate the influence of local quality on the quality of the whole three-dimensional curved surface, and effectively improve the identification rate of unqualified products. It improves the shortcomings that the traditional three-dimensional quality classification method cannot be applied to large-scale point cloud data and the defect that the impact of sub-region quality changes on the overall surface is difficult to evaluate.
Description
technical field [0001] The invention relates to quality analysis of part surfaces, in particular to a method, system and storage medium for classifying the quality of three-dimensional curved surfaces based on point cloud data. Background technique [0002] During the manufacturing process, the surface quality of the part has a significant impact on the overall quality of the product. Surface quality classification is an important method to identify the surface quality of parts, and it is also a key process in product quality control. The traditional evaluation index of the surface quality of parts is the surface profile, that is, the change of the measured actual profile relative to the ideal profile. Measurement methods include profiling device measurement, cross-sectional profile sample measurement, optical tracking profile measuring instrument measurement, and three-coordinate measurement. However, due to the arbitrariness of the three-dimensional surface, it is diffic...
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IPC IPC(8): G06K9/62G06T7/11G06V10/764G06V10/46G06N3/04G06N3/08G01B21/20
CPCG06T7/11G06N3/04G06N3/08G01B21/20G06T2207/10028G06V10/462G06F18/24
Inventor 赵晨黄德林
Owner 晶仁光电科技(苏州)有限公司