{"title":"HR-IDF: Hessian-Regularized Implicit Displacement Fields for high precision industrial assembly representation","authors":"Linxu Guo , Yutaka Ohtake , Tatsuya Yatagawa , Tetsuya Shimmyo , Shoichiro Hosomi , Kazutoshi Miyamoto","doi":"10.1016/j.cag.2025.104442","DOIUrl":null,"url":null,"abstract":"<div><div>Representing high-precision industrial assemblies characterized by complex structural features remains challenging. In this paper, we propose Hessian-Regularized Implicit Displacement Fields (HR-IDF), a framework that integrates a two-scale neural implicit representation with Hessian-based regularization. In a coarse-to-fine manner, our method generates a smooth base surface from mesh-sampled points and then refines it with a high-frequency displacement field to capture fine geometric details. Moreover, we introduce a relaxed off-surface loss that helps preserve a more consistent gradient in the generated SDF field, while suppressing ghost geometry and improving representation stability and fidelity. Extensive experiments on complex industrial assemblies and 3D models demonstrate that HR-IDF achieves a reliable solution for high-precision industrial applications.</div></div>","PeriodicalId":50628,"journal":{"name":"Computers & Graphics-Uk","volume":"133 ","pages":"Article 104442"},"PeriodicalIF":2.8000,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Graphics-Uk","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0097849325002833","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Representing high-precision industrial assemblies characterized by complex structural features remains challenging. In this paper, we propose Hessian-Regularized Implicit Displacement Fields (HR-IDF), a framework that integrates a two-scale neural implicit representation with Hessian-based regularization. In a coarse-to-fine manner, our method generates a smooth base surface from mesh-sampled points and then refines it with a high-frequency displacement field to capture fine geometric details. Moreover, we introduce a relaxed off-surface loss that helps preserve a more consistent gradient in the generated SDF field, while suppressing ghost geometry and improving representation stability and fidelity. Extensive experiments on complex industrial assemblies and 3D models demonstrate that HR-IDF achieves a reliable solution for high-precision industrial applications.
期刊介绍:
Computers & Graphics is dedicated to disseminate information on research and applications of computer graphics (CG) techniques. The journal encourages articles on:
1. Research and applications of interactive computer graphics. We are particularly interested in novel interaction techniques and applications of CG to problem domains.
2. State-of-the-art papers on late-breaking, cutting-edge research on CG.
3. Information on innovative uses of graphics principles and technologies.
4. Tutorial papers on both teaching CG principles and innovative uses of CG in education.