作者:孙进，马昊天，雷震霆，梁立
Authors:SUNJin,MAHaotian,LEIZhenting,LIANGLi摘要:针对碗状文物模型由于碎片缺失导致的逆向几何重建保真度不高的问题 , 为此提出了 一 种基于双重 判别解码器的三维点云形状补全网络 。首先基于编码解码器构建基本点云生成网络 ,然后根据生成对抗网络框架 优化解码器结构 ,通过将全局特征进行解码获取目标骨架点云 ,保证点云的全局特征 ,进而在对骨架点云的基础上 进 一步进行局部点云细化生成判别 ,保证目标点云的局部特征 。最后面向特征缺失拼接模型搭建双分支形状补全 网络 。实验结果表明在公开数据集 ShapeNet 的点云补全实验中 ,本文方法的平均误差更小 ,相较对比网络 ,本文方 法在碗状文物模型的三维形状补全任务更好 ,平均倒角距离提高了 20. 2% ,为后续的模型逼真化提供了 一个基础 , 具有更强的性能和良好的应用价值。
Abstract:Aiming at the problem of low fidelity of reverse geometric reconstruction of bowl-shaped cultural relics model due to the missing fragments , a 3D point cloud shape completion network based on double discrimination decoder is proposed. Firstly , a basic point cloud generation network is built based on the codec , and then the decoder structure is optimized according to the framework of generation confrontation network. By decoding the global features , the target skeleton point cloud is obtained , and the global features of the point cloud are guaranteed. Then , on the basis of the skeleton point cloud , the local point cloud generation is further refined to ensure the local features of the target point cloud. Finally , a two-branch shape completion network is built for the feature missing splicing model. The experimental results show that the average error of this method is smaller in the point cloud completion experiment of ShapeNet , an open data set. Compared with the comparison network , this method is better in completing the three-dimensional shape of the bowl-shaped cultural relics model , and the average chamfer distance is increased by 20. 2% , which provides a foundation for the subsequent model fidelity , and has stronger performance and good application value.

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