🤖 AI Summary
This work addresses the challenge that modern 3D reconstruction and generation methods often yield dense, noisy, and non-manifold meshes, which are ill-suited for applications such as simulation and AR/VR that demand efficient and reliable geometry. To tackle this, the authors propose a feature-aware quadric error metric (FA-QEM) simplification framework that integrates geometric deviation, boundary curvature, and normal consistency into a multi-objective quadratic error function. Coupled with optimal vertex placement and a continuous texture mapping transfer strategy, the method effectively preserves sharp geometric features while achieving substantial mesh simplification. Experiments demonstrate that FA-QEM significantly reduces geometric error, enhances visual fidelity, and improves computational efficiency on both AI-generated and real-world datasets, exhibiting strong robustness and practical utility.
📝 Abstract
The rapid growth of 3D content from modern reconstruction and generative pipelines, such as neural rendering and large-scale 3D asset generation, has led to an abundance of dense, noisy, and often non-manifold meshes. While these representations achieve high visual fidelity, their complexity poses significant challenges for downstream applications in simulation, AR/VR, and scientific computing, where efficient and reliable geometry is essential. This necessitates mesh simplification methods that are not only fast and robust to "in-the-wild" inputs, but also capable of preserving fine geometric structures and high-quality appearance. In this paper, we propose Feature-Aware Quadric Error Metric (FA-QEM), a comprehensive mesh simplification pipeline designed for modern 3D assets. Our approach introduces a novel multi-term quadric error formulation that jointly encodes geometric deviation, boundary curvature, and surface normal consistency, enabling optimal vertex placement that preserves sharp features even under aggressive simplification. Furthermore, we show that high-fidelity geometric simplification significantly improves downstream appearance transfer, serving as a superior front-end for texture mapping via successive mapping techniques. We conduct extensive evaluations on both AI-generated meshes and large-scale real-world datasets, including Thingi10K and the Real-World Textured Things dataset. Our results demonstrate that FA-QEM achieves consistently lower geometric error, better visual fidelity, and substantially faster runtimes compared to existing methods, while maintaining robustness across diverse and challenging inputs. These properties make FA-QEM a practical and effective component for scalable 3D reconstruction and generation pipelines.