End-to-end high-precision industrial part shape modeling method

A modeling method and high-precision technology, which is applied in neural learning methods, biological neural network models, image data processing, etc., can solve the problem that the shape modeling of industrial parts cannot adapt to various data environments, and shorten debugging Time, large adaptability, simple effect of model debugging

Active Publication Date: 2020-07-28
视研智能科技(广州)有限公司
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AI Technical Summary

Benefits of technology

This patented technique allows for precise extraction of shapes within 3-dimensional points cloud models by accurately detecting edge boundaries between specific areas or segments. By comparing these boundary values against templates stored during training, it becomes possible to estimate how well different objects are located inside them without being affected by any external factors such as lightning strikes or other environmental influences. Additionally, this approach provides an efficient way to generate synthetic images (images) through fitting preprocessed surfaces onto those generated from 3-dimensionality space. Overall, this new process simplifies the analysis and interpretation of complex three dimensional structures while maintaining their quality level.

Problems solved by technology

This patented describes an algorithm for creating accurate shapes from image scans obtained by robots during manufacturing processes that involve joining different materials together without causing damage caused by external factors like lightning strikes or impact pressure waves. Current algorithms have limitations due to their lack of ability to handle diverse environmental conditions, including varying levels of background clutter (noise) and variations between objects being joined). Additionally, existing techniques require multiple steps before they become effective at accurately extracting desired patterns.

Method used

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  • End-to-end high-precision industrial part shape modeling method
  • End-to-end high-precision industrial part shape modeling method

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Embodiment 1

[0044] This embodiment provides an end-to-end high-precision shape modeling method for industrial parts. Such as figure 1 As shown, it mainly includes: S1: edge extraction, S2: building topological relationship between points, S3: point feature extraction, S4: point optimization. The four modules will be described separately below.

[0045] S1: Initial edge point extraction.

[0046] S1.1: Generate a depth map using the input point cloud.

[0047] S1.2: Mark the area of ​​each part and its internal edge structure on the depth map as the ground truth for model training.

[0048] S1.3: Use Mask-RCNN on the depth map for target-level detection and semantic segmentation, and at the same time predict the distance between each pixel and its nearest edge point, referred to as the distance map.

[0049] S1.4: Obtain the area of ​​each target detected by S1.3. In the distance map for this region, set pixel values ​​less than 2 to 1, and to 0 otherwise. The result is the initial edge

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Abstract

The invention relates to an end-to-end high-precision industrial part shape modeling method. The method comprises the steps of S1, obtaining an edge graph through edge point extraction; s2, constructing an inter-point topological relation according to the edge graph; s3, performing feature point extraction on the point graph belonging to each target; and S4, performing coding-decoding point location optimization on the feature points to realize model parameter optimization. The method has extremely high adaptability and accuracy, and concise and accurate shape lines can be stably extracted under the conditions of data noise, shielding, shadows and the like; the method has great adaptability and can be directly applied to geometric modeling of various types of parts; according to the method, the problem that the shape lines cannot be stably extracted under the conditions of data noise, shielding, shadows and the like is effectively solved; meanwhile, model debugging is simple, and compared with a traditional heuristic shape modeling technology, the algorithm debugging time is greatly shortened.

Description

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Claims

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Application Information

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Owner 视研智能科技(广州)有限公司
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