中圖分類號：TP391.41 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.223446 中文引用格式： 季建杰,，劉杰,，邵劍飛，等. 基于動態(tài)圖卷積的點云補全網(wǎng)絡(luò)[J]. 電子技術(shù)應(yīng)用,，2023,，49(6)：18-23. 英文引用格式： Ji Jianjie，Liu Jie,，Shao Jianfei,，et al. Point cloud completion network based on dynamic graph convolution[J]. Application of Electronic Technique，2023,，49(6)：18-23.
Point cloud completion network based on dynamic graph convolution
Ji Jianjie1，Liu Jie2,，Shao Jianfei1，Zhang Jianhua3
(1.Faculty of Information Engineering and Automation,，Kunming University of Science and Technology,， Kunming 650504,， China,； 2.Yunnan Police College,， Kunming 650223,，China,； 3.Yunnan Zhongkan Surveying and Mapping Engineering Company， Kunming 650034,，China)
Abstract： Most traditional deep learning point cloud complement learning methods only use the global features and ignore the local features. In order to better extract and use the local features of point cloud, an end-to-end cloud completion network based on deep learning is proposed in this paper. On the basis of point cloud completion network (PCN), the coding part introduces dynamic graph convolution (DGCNN) improved for local features. The edge convolution of multiple different dimensions is used to extract more abundant local features, and weaken the characteristics of the far point according to the distance. Then the network structure is optimized with the idea of deep residual network connection to achieve the fusion of multi-scale features, and the mean pooling method is added to compensate for the information loss caused by global pooling. In the decoder part, FoldingNet was used to make the output point cloud complete. The experimental results show that the point cloud completion network is partially improved compared with PCN and other point cloud completion networks, which verifies the effectiveness of the new method.
Key words : image processing,；3D point cloud,；convolutional neural networks,；shape completion
