用高斯点云实现动态模型拓扑一致性重建，支持精准3D关键点追踪。
TagSplat: Topology-Aware Gaussian Splatting for Dynamic Mesh Modeling and Tracking
- 引入显式拓扑结构的高斯点云，保持网格流形一致性。
- 在动态序列上实现拓扑一致的网格重建，精度显著优于现有方法。
- 适合需要精确动画建模与关键点跟踪的应用场景。
拓扑一致的动态模型序列对动画和模型编辑等应用至关重要。然而，现有4D重建方法在生成高质量拓扑一致网格方面仍面临挑战。为此，我们提出一种基于高斯点云的拓扑感知动态重建框架。通过引入显式编码空间连通性的高斯拓扑结构，实现了拓扑感知的点云加密与剪枝，从而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，可微网格光栅化提升了网格质量。实验结果表明，本方法在重建拓扑一致的网格序列方面显著优于现有方法，且生成的网格支持精确的3D关键点追踪。
原文摘要 · Abstract (English)
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/
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