GarmentGS：用于高保真非水密3D服装重建的点云引导高斯溅射
GarmentGS: Point-Cloud Guided Gaussian Splatting for High-Fidelity Non-Watertight 3D Garment Reconstruction
AI总结：
针对3D Gaussian Splatting难以重建高保真非水密服装的问题，提出GarmentGS，利用10分钟级稠密点云引导高斯基元分布，实现高精度单层网格重建、快速训练与实时渲染。
摘要：
传统的3D服装创建需要大量人工操作，导致时间和人力成本较高。近年来，3D Gaussian Splatting在三维场景重建与渲染方面取得了突破性进展，受到广泛关注，并为3D服装重建开辟了新途径。然而，由于高斯基元具有非结构化和不规则的特性，很难重建高保真、非水密的3D服装。本文提出GarmentGS，这是一种稠密点云引导方法，能够以较高几何精度重建高保真服装表面，并生成非水密的单层网格。该方法引入了一个快速稠密点云重建模块，可在10分钟内完成服装点云重建，而传统方法需要数小时。此外，我们利用稠密点云引导高斯基元的移动、展平和旋转，使其更好地分布在服装表面，从而获得更优的渲染效果和几何精度。通过数值和视觉对比，本方法实现了快速训练和实时渲染，同时保持了具有竞争力的质量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
