TopoSurfel：闭合高斯面元与网格间的循环以实现表面重建
TopoSurfel: Closing the Loop between Gaussian Surfels and Meshes for Surface Reconstruction
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- University of Science and Technology of China（中国科学技术大学）
- Deep Space Exploration Laboratory（深空探测实验室）
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中文总结 AI 辅助
针对3D高斯溅射直接提取高保真表面易产生伪影和浮点数的问题，提出闭合高斯面元与网格循环的TopoSurfel框架，通过动态生成代理网格及相关策略实现高质量表面重建与新视图合成。
中文摘要 AI 辅助
3D高斯溅射（3D Gaussian Splatting）在新视图合成领域已取得显著成功，但由于其离散且非结构化的特性，直接从3DGS中提取高保真表面仍具挑战性。现有基于3DGS的重建方法通常依赖多视图几何一致性或局部约束，优化过程中缺乏显式结构化几何先验，导致这些方法难以解决结构歧义问题，进而产生伪影和浮点数（floaters），尤其在无纹理或遮挡区域更为明显。为解决这一局限，我们提出TopoSurfel，这是一种闭合高斯面元（Gaussian surfels）与连续网格（meshes）间循环的新型框架。与近期通过引入辅助神经网络或额外的逐高斯参数将网格提取融入可微流程的方法不同，我们通过不可训练的可微等值面提取过程动态生成连续代理网格。利用这种可微连接，我们引入了网格引导的面元演化策略，包括法向对齐和几何感知的密度控制，以有效抑制浮点数并填补表面孔洞。此外，为解决大规模环境中的初始化难题，我们提出空间感知的混合重初始化策略，确保在复杂场景中实现稳健重建。大量实验表明，TopoSurfel在保持高质量基于网格的新视图合成的同时，达到了具有竞争力的几何重建精度。本方法的代码可在此https URL获取。
英文摘要
3D Gaussian Splatting has achieved remarkable success in novel view synthesis. However, extracting high-fidelity surfaces directly from 3DGS remains challenging due to its discrete and unstructured nature. Existing 3DGS-based reconstruction methods typically rely on multi-view geometric consistency or local constraints. Without an explicit structured geometric prior during optimization, these methods often struggle to resolve structural ambiguities, leading to artifacts and floaters, particularly in textureless or occluded regions. To address this limitation, we propose TopoSurfel, a novel framework that closes the loop between Gaussian surfels and continuous meshes. Unlike recent methods that incorporate mesh extraction into the differentiable pipeline by introducing auxiliary neural networks or extra per-Gaussian parameters, we dynamically extract a continuous proxy mesh via a non-trainable differentiable iso-surfacing process. Leveraging this differentiable connection, we introduce a mesh-guided surfel evolution strategy, including normal alignment and geometry-aware density control, to effectively suppress floaters and fill surface holes. Furthermore, to address the initialization challenges in large-scale environments, we propose a spatially aware hybrid re-initialization strategy that ensures robust reconstruction across complex scenes. Extensive experiments demonstrate that TopoSurfel achieves competitive geometric reconstruction accuracy while maintaining high-quality mesh-based novel view synthesis. The code for our method is available at https://github.com/Fan-Treasure/TopoSurfel.
