TagSplat: 基于拓扑的高斯点散布用于动态网格建模与追踪
TagSplat: Topology-Aware Gaussian Splatting for Dynamic Mesh Modeling and Tracking
AI总结：
TagSplat通过拓扑感知的高斯点散布方法，实现了高质量的动态网格重建与追踪，提升拓扑一致性与网格质量。
摘要：
拓扑一致的动态模型序列对于动画和模型编辑等应用至关重要。然而，现有的4D重建方法在生成高质量拓扑一致的网格方面面临挑战。为了解决这一问题，我们提出了一种基于高斯点散布的拓扑感知动态重建框架。我们引入了高斯拓扑结构，该结构明确编码空间连接性。这种结构使拓扑感知的密集化和修剪成为可能，保持高斯表示的流形一致性。时间正则化项进一步确保了时间上的拓扑一致性，而可微的网格光栅化提高了网格质量。实验结果表明，我们的方法重建出的拓扑一致网格序列在准确性上显著优于现有方法。此外，所得到的网格能够实现精确的3D关键点追踪。项目页面：https://haza628.github.io/tagSplat/
英文摘要：
Topology-consistent dynamic model sequences are essential for applications such as animation and model editing. However, existing 4D reconstruction methods face challenges in generating high-quality topology-consistent meshes. To address this, we propose a topology-aware dynamic reconstruction framework based on Gaussian Splatting. We introduce a Gaussian topological structure that explicitly encodes spatial connectivity. This structure enables topology-aware densification and pruning, preserving the manifold consistency of the Gaussian representation. Temporal regularization terms further ensure topological coherence over time, while differentiable mesh rasterization improves mesh quality. Experimental results demonstrate that our method reconstructs topology-consistent mesh sequences with significantly higher accuracy than existing approaches. Moreover, the resulting meshes enable precise 3D keypoint tracking. Project page: https://haza628.github.io/tagSplat/
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。
