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Open Access

ARTICLE

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

Jiming Lan1, Bo Zeng1,*, Suiqun Li1, Weihan Zhang1, Xinyi Shi2
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
* Corresponding Author: Bo Zeng. Email: email
(This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.060260

Received 28 October 2024; Accepted 16 January 2025; Published online 21 February 2025

Abstract

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-QEM is particularly effective for applications requiring high-fidelity simplification while retaining critical features.

Keywords

Deep learning; mesh simplification; quadric error metrics (QEM); salient feature preservation; point sampling

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