A Deep Learning-Based Salient Feature-Preserving Algorithm for Mesh Simplification  

作　　者：Jiming Lan Bo Zeng Suiqun Li Weihan Zhang Xinyi Shi 

机构地区：[1]Sichuan Key Provincial Research Base of Intelligent Tourism,Sichuan University of Science and Engineering,Zigong,644005,China [2]School of Computer Science and Engineering,Sichuan University of Science and Engineering,Zigong,644005,China

出　　处：《Computers, Materials & Continua》2025年第5期2865-2888,共24页计算机、材料和连续体(英文)

基　　金：Our research was funded by the Sichuan Key Provincial Research Base of Intelligent Tourism(No.ZHZJ23-02);supported by the Scientific Research and Innovation Team Program of Sichuan University of Science and Engineering(No.SUSE652A006);Additional support was provided by the National Cultural and Tourism Science and Technology Innovation Research andDevelopment Project(No.202417);the Lantern Culture and Crafts Innovation Key Laboratory Project of the Sichuan ProvincialDepartment of Culture and Tourism(No.SCWLCD-A02).

摘　　要：The Quadric Error Metrics(QEM)algorithm is a widely used method for mesh simplification;however,it often struggles to preserve high-frequency geometric details,leading to the loss of salient features.To address this limitation,we propose the Salient Feature Sampling Points-based QEM(SFSP-QEM)—also referred to as the Deep Learning-Based Salient Feature-Preserving Algorithm for Mesh Simplification—which incorporates a Salient Feature-Preserving Point Sampler(SFSP).This module leverages deep learning techniques to prioritize the preservation of key geometric features during simplification.Experimental results demonstrate that SFSP-QEM significantly outperforms traditional QEM in preserving geometric details.Specifically,for general models from the Stanford 3D Scanning Repository,which represent typical mesh structures used in mesh simplification benchmarks,the Hausdorff distance of simplified models using SFSP-QEM is reduced by an average of 46.58% compared to those simplified using traditional QEM.In customized models such as the Zigong Lantern used in cultural heritage preservation,SFSP-QEM achieves an average reduction of 28.99% in Hausdorff distance.Moreover,the running time of this method is only 6%longer than that of traditional QEM while significantly improving the preservation of geometric details.These results demonstrate that SFSP-QEMis particularly effective for applications requiring high-fidelity simplification while retaining critical features.

关 键 词：Deep learning mesh simplification quadric error metrics(QEM) salient feature preservation point sampling 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]