用3D高斯点云实现头发丝级几何重建，提升真实感与连通性。
HairGS: Hair Strand Reconstruction based on 3D Gaussian Splatting
- 基于可微分高斯渲染，分阶段重建头发几何并合并成连续发丝。
- 在合成与真实数据集上均实现小时级高效重建，头发拓扑准确率显著提升。
- 提出新评估指标衡量发丝连通性，适合虚拟人建模与影视特效应用。
人发重建是计算机视觉中的难题，对虚拟现实和数字人建模具有重要意义。近年来，3D高斯溅射（3DGS）提供了高效且显式的场景表示，天然契合发丝结构。本文将3DGS框架拓展至多视角图像下的发丝级几何重建。所提多阶段流程首先利用可微分高斯光栅化器重建精细头发几何，再通过新颖的合并策略将独立高斯片段整合为连贯发丝，并在光照监督下进行精细化与生长优化。现有方法通常仅在几何层面评估重建质量，却忽视发丝间的连接性与拓扑结构。为此，本文提出一种新评估指标，作为发丝拓扑精度的代理指标。在合成与真实数据集上的大量实验表明，该方法能稳健处理多种发型，实现高效重建，通常在一小时内完成。项目页面见：https://yimin-pan.github.io/hair-gs/
原文摘要 · Abstract (English)
Human hair reconstruction is a challenging problem in computer vision, with growing importance for applications in virtual reality and digital human modeling. Recent advances in 3D Gaussians Splatting (3DGS) provide efficient and explicit scene representations that naturally align with the structure of hair strands. In this work, we extend the 3DGS framework to enable strand-level hair geometry reconstruction from multi-view images. Our multi-stage pipeline first reconstructs detailed hair geometry using a differentiable Gaussian rasterizer, then merges individual Gaussian segments into coherent strands through a novel merging scheme, and finally refines and grows the strands under photometric supervision. While existing methods typically evaluate reconstruction quality at the geometric level, they often neglect the connectivity and topology of hair strands. To address this, we propose a new evaluation metric that serves as a proxy for assessing topological accuracy in strand reconstruction. Extensive experiments on both synthetic and real-world datasets demonstrate that our method robustly handles a wide range of hairstyles and achieves efficient reconstruction, typically completing within one hour. The project page can be found at: https://yimin-pan.github.io/hair-gs/
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