中圖分類號： TP391.7 文獻(xiàn)標(biāo)識碼： A DOI：10.16157/j.issn.0258-7998.200892 中文引用格式： 何瑞函，蔡勇,，張建生. 基于優(yōu)化多視角圖像采集的點(diǎn)云分類[J].電子技術(shù)應(yīng)用,，2021，47(10)：82-85. 英文引用格式： He Ruihan,，Cai Yong,，Zhang Jiansheng. Point cloud classification based on optimized multi-view image acquisition[J]. Application of Electronic Technique，2021,，47(10)：82-85.
Point cloud classification based on optimized multi-view image acquisition
He Ruihan1,，2，Cai Yong1,，2,，Zhang Jiansheng1，2
1.School of Manufacturing Science and Engineering,，Southwest University of Science and Technology,，Mianyang 621010，China,； 2.Key Laboratory of Testing Technology for Manufacturing Process,，Mianyang 621010，China
Abstract： In the 3D recognition method based on 2D multi-perspective, multiple 2D projected images can be used to represent the feature information of 3D model. However, the features of projected images from different perspectives are different, and the learning efficiency of the neural network is also different. The convolutional neural network can map the features of images, and this method can be used to analyze this problem. The importance of projection features of different perspectives in the convolutional neural network was analyzed in the mix-view data set, and the collection density of the mix-view data set was optimized according to the different importance. The final experimental results show that the classification accuracy of 2D images generated from different perspectives is different, among which the classification accuracy of overhead projection is the worst, and the optimized data set achieves the optimal classification accuracy in the same neural networks model.
Key words : 3D point cloud,；multi-view image,；convolution neural network；image classification
