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Sign point hole filling method based on neural network in tri-D scanning point cloud

A three-dimensional scanning and neural network technology, which is applied to the field of filling the holes of landmark points based on neural network in the three-dimensional scanning point cloud. , The effect of point cloud feature representation is good and the method is simple

Inactive Publication Date: 2008-02-20
海安江理工技术转移中心有限公司 +1
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Problems solved by technology

This type of method has a wide range of applications, and it has better results for nonlinear data, noisy data, and data with unclear pattern characteristics, but it has shortcomings: many operations, and each hole must be artificially selected. Sample point set; the density of points in the repaired area is inconsistent with the surrounding; for some feature areas with large curvature changes, the feature performance is not good enough

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  • Sign point hole filling method based on neural network in tri-D scanning point cloud
  • Sign point hole filling method based on neural network in tri-D scanning point cloud
  • Sign point hole filling method based on neural network in tri-D scanning point cloud

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Embodiment Construction

[0035] A neural network-based method for filling holes in landmark points in a 3D scanning point cloud:

[0036] Step 1: For the hole formed by the marker points pasted on the surface of the head of Venus, in the 3D scanning point cloud, when obtaining the sample point set around the hole, according to the 3D coordinates of the marker points (x b ,y b ,z b ), take the data points in the cube centered on the marker point as the sample point P for hole filling s (s=0, 1, ..., t), the 8 vertices of the cube are (x b -r,y b -r,z b -r), (x b -r,y b -r,z b +r), (x b -r,y b + r, z b -r), (x b -r,y b + r, z b +r), (x b +r,y b -r,z b -r), (x b +r,y b -r,z b +r), (x b +r,y b + r, z b -r), (x b +r,y b + r, z b +r), where r is the selected domain value, in order to obtain a suitable sample point set, take 1.2 to 1.5 times the radius of the marker point;

[0037] Step 2: According to the sample point data P s , training the neural network for filling holes, the n...

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Abstract

The utility model provides a point cloud hole filling method that is based on three-dimensional scanning of neural networks, ensures the continuity of the filled holes data and the surrounding data, and has better point cloud characteristic. The utility model has the advantage of simple method. The utility model is mainly applied to the application situations of filling holes with various complicated curved surface shapes produced by marking points in three-dimensional scanning system. With the neural network method of the utility model, a network with filled holes is obtained, sampling points in the hole regions are sampled subsequently based on the density of the hole boundary points, further the points of filling holes are adjusted according to the curvature of the points to achieve smooth filling of holes.

Description

technical field [0001] The invention relates to a method for repairing three-dimensional graphics, in particular to a method for filling holes in marker points based on neural networks in a three-dimensional scanning point cloud. Background technique [0002] Reverse Engineering (Reverse Engineering, RE) technology is a new technology that appeared in the field of advanced manufacturing in the late 1980s. It generally includes four basic links: three-dimensional shape detection and conversion (obtaining physical data), data preprocessing (point Cloud processing, recognition, multi-view splicing), CAD model establishment (surface reconstruction), CAM part forming, the basic flow chart is shown in Figure 1. In the process of 3D shape detection and conversion, the 3D digital scanner is used to quickly scan and measure the surface of the physical model. On the premise of satisfying the discrete sampling speed and data quality, the 3D discrete data of the product is obtained. In ...

Claims

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Application Information

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IPC IPC(8): G06T11/00G06N3/02
Inventor 达飞鹏谷继兵盖绍彦朱正键杨伟光
Owner 海安江理工技术转移中心有限公司
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