2025/05/04 by Tang, Zhihao, Shenghao Yang, Yang, Shenghao +4
Earth and Planetary Sciences · Engineering · #3D Shape Modeling and Analysis #3D Surveying and Cultural Heritage #3D reconstruction #3d model #Additive Manufacturing and 3D Printing Technologies #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Gaussian #Point (geometry) #Point cloud #Rendering (computer graphics) #Surface reconstruction
paper · pdf · doi:10.48550/arxiv.2505.02126
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.