HR-IDF: Hessian-Regularized Implicit Displacement Fields for high precision industrial assembly representation
Year: 2025
Type: article
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.
Source: Computers & Graphics
Authors: Linxu Guo, Yutaka Ohtake, Tatsuya Yatagawa, Tetsuya Shimmyo, Shoichiro Hosomi, Kazutoshi Miyamoto
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,770
OpenAlex ID: W4414400248