计算机工程与应用2024，Vol.60Issue(9)：272-282,11.DOI:10.3778/j.issn.1002-8331.2212-0355
双分支结构的多层级三维点云补全
Multi-Level 3D Point Cloud Completion with Dual-Branch Structure
摘要
Abstract
In order to alleviate the problem that the existing point cloud completion methods are difficult to balance local features and global features in the feature extraction process,this paper proposes a multi-level point cloud completion algo-rithm with double branch structure.Two independent branch networks are used to extract the local feature information and global feature information of the input point cloud respectively,and the two feature information are concatenated to form a feature vector.The five levels combinate perceptron is used to map the feature vector into multiple dimensions,and the multi-dimensional feature information is extracted and integrated into the final feature vector.Then,the pyramid structure is used to decode the final feature vector in 256,512 and 1 024 feature dimensions,and the point clouds with three differ-ent resolutions are predicted.Finally,the discriminator network is introduced to optimize the network by jointly training the adversarial loss generated by the discriminator and the completion loss generated by the hierarchical reconstruction point cloud.Experiments on ShapeNet dataset show that the algorithm significantly improves the accuracy of point cloud completion.In addition,a relatively complete object shape can be recovered when a large area of point cloud is missing.关键词
三维点云/形状补全/深度学习/双分支结构/鉴别器网络Key words
three-dimensional point cloud/shape completion/deep learning/dual-branch structure/discriminator network分类
信息技术与安全科学引用本文复制引用
邱云飞,王宜帆..双分支结构的多层级三维点云补全[J].计算机工程与应用,2024,60(9):272-282,11.基金项目
国家自然科学基金(71771111). （71771111）
