Implicit surface reconstruction from point cloud data based on generalized polynomials neural network

2009 ◽  
Vol 29 (8) ◽  
pp. 2043-2045 ◽  
Author(s):  
Xiu-chun XIAO ◽  
Xiao-hua JIANG ◽  
Yu-nong ZHANG
Keyword(s):  
Neural Network ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Implicit Surface ◽  
Generalized Polynomials ◽  
Cloud Data

scholarly journals Learning Collision Situation to Convolutional Neural Network Using Collision Grid Map Based on Probability Scheme

2020 ◽  
Vol 10 (2) ◽  
pp. 617
Author(s):  
Jo ◽  
Moon
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Point Cloud ◽  
Probability Method ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Location Data ◽  
Grid Map ◽  
Permutation Problem

In this paper, a Collision Grid Map (CGM) is proposed by using 3d point cloud data to predict the collision between the cattle and the end effector of the manipulator in the barn environment. The Generated Collision Grid Map using x-y plane and depth z data in 3D point cloud data is applied to a Convolutional Neural Network to predict a collision situation. There is an invariant of the permutation problem, which is not efficiently learned in occurring matter of different orders when 3d point cloud data is applied to Convolutional Neural Network. The Collision Grid Map is generated by point cloud data based on the probability method. The Collision Grid Map scheme is composed of a 2-channel. The first channel is constructed by location data in the x-y plane. The second channel is composed of depth data in the z-direction. 3D point cloud is measured in a barn environment and created a Collision Grid Map. Then the generated Collision Grid Map is applied to the Convolutional Neural Network to predict the collision with cattle. The experimental results show that the proposed scheme is reliable and robust in a barn environment.

Automobile Panel Reverse Engineering Design Based on NURBS Surface Reconstruction Technique

2011 ◽  
Vol 128-129 ◽  
pp. 1341-1344
Author(s):  
Hong Yuan Zhang ◽  
Peng He
Keyword(s):  
Engineering Design ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Reconstruction Technique ◽  
Point Cloud Data ◽  
Data Sampling ◽  
Nurbs Surface ◽  
Cloud Data ◽  
Fast Speed ◽  
Automobile Panel

This paper takes automobile panel as the study object, and adopts non-contact 3-D scanner to obtain the point cloud data of the automobile panel for point cloud data sampling and noise reduction processing. It obtains NURBS surface fitting through detecting of curvature and grid. Reverse design can improve the product prototype in a fast speed and provide an important way of automobile panel development.

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