计算机工程2024，Vol.50Issue(5)：220-228,9.DOI:10.19678/j.issn.1000-3428.0067724
基于隐式表达的服装三维重建
Implicit-Expression-based 3D Reconstruction of Clothing
摘要
Abstract
Owing to the rapid development of Internet shopping in recent years,clothing items have appeared increasingly on major platforms.Generating Three-Dimensional(3D)models of garments using 3D reconstruction technology allows consumers to better understand the gestural information of garments.This study examines the 3D reconstruction technology of clothing and proposes a 3D clothing-reconstruction model based on implicit expressions.An occupancy function obtained via neural-network learning is used as the implicit expression of the garment 3D model,and mapping is performed between the 3D coordinates and model shape.Existing 3D reconstruction methods must fit complex surface models,which consumes a significant amount of resource,whereas implicit-expression-based 3D reconstruction algorithms require neither parameterization nor meshing,which accelerates the operation of the algorithm.To further improve the 3D reconstruction effect,the current best-performing PointMetaBase-L network model and the offset attention module are used as the feature-extraction network of the model.Between them,the PointMeta-Base-L network model proposes the PointMeta meta-architecture of the Set Abstraction layer based on the existing point cloud feature-extraction network and selects the best practices of four modules in the PointMeta meta-architecture to constitute the set of the PointMetaBase-L network model.This is performed by analyzing the abstraction layer while introducing a planar feature-projection module to enhance the local information of the features.In the feature-decoding stage,the occupancy probabilities of sampled points in 3D space are obtained using a weighted averaging algorithm via a feature-weighting network.A high-precision mesh-reconstruction model is extracted from the occupancy probabilities of the sampled points using the March Cubes algorithm based on regional growth.Experimental results show that compared with the occupancy network,the improved model improves the intersection ratio,chamfer distance,normal consistency,and F1 value by 48.83％,55.17％,4.27％,and 79.10％,respectively.关键词
隐式表达/三维重建/PointMetaBase-L网络模型/偏移注意力/特征权重网络/区域增长/Marching Cubes算法Key words
implicit expression/3D reconstruction/PointMetaBase-L network model/offset attention/feature weight network/region growth/Marching Cubes algorithm分类
信息技术与安全科学引用本文复制引用
费煜哲,蔡欣,赵鸣博,杨圣豪..基于隐式表达的服装三维重建[J].计算机工程,2024,50(5):220-228,9.基金项目
国家自然科学基金面上项目(61971121). （61971121）
