用点云引导高斯点，快速重建高精度非封闭服装三维模型
GarmentGS: Point-Cloud Guided Gaussian Splatting for High-Fidelity Non-Watertight 3D Garment Reconstruction
- 用密集点云引导高斯点运动与分布，提升表面贴合度
- 10分钟完成点云重建，训练快、渲染实时，质量不输传统方法
- 适合需要快速生成高质量服装3D模型的设计师与开发者
传统3D服装建模依赖大量人工操作，耗时耗力。近年来，3D高斯点阵在场景重建与渲染中取得突破，为3D服装重建开辟新路径。然而，由于高斯原语无结构且不规则，难以实现高保真、非封闭的3D服装重建。本文提出GarmentGS，一种基于密集点云引导的方法，可实现高几何精度的非封闭单层网格重建。该方法引入快速点云重建模块，10分钟内完成服装点云重建，远快于传统需数小时的方法。同时，利用密集点云指导高斯原语的移动、展平与旋转，使其更优分布在服装表面，显著提升渲染效果与几何精度。数值与视觉对比显示，本方法实现快速训练与实时渲染，同时保持竞争力的重建质量。
原文摘要 · Abstract (English)
Traditional 3D garment creation requires extensive manual operations, resulting in time and labor costs. Recently, 3D Gaussian Splatting has achieved breakthrough progress in 3D scene reconstruction and rendering, attracting widespread attention and opening new pathways for 3D garment reconstruction. However, due to the unstructured and irregular nature of Gaussian primitives, it is difficult to reconstruct high-fidelity, non-watertight 3D garments. In this paper, we present GarmentGS, a dense point cloud-guided method that can reconstruct high-fidelity garment surfaces with high geometric accuracy and generate non-watertight, single-layer meshes. Our method introduces a fast dense point cloud reconstruction module that can complete garment point cloud reconstruction in 10 minutes, compared to traditional methods that require several hours. Furthermore, we use dense point clouds to guide the movement, flattening, and rotation of Gaussian primitives, enabling better distribution on the garment surface to achieve superior rendering effects and geometric accuracy. Through numerical and visual comparisons, our method achieves fast training and real-time rendering while maintaining competitive quality.
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