发表机构
Zhejiang University; Hefei University of Technology; Shenzhen Loop Area Institute（浙江大学; 合肥工业大学; 深圳河套学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究增量式在线场景重建问题，提出通过直接对密集几何高斯表示三角剖分来重建和更新显式网格的方法，还给出直接网格化算法及相关约束，实验证明该方法性能优于传统高斯方法。
AI 中文摘要
增量式场景重建对实际应用至关重要。尽管3D高斯点云渲染展现出强大潜力，但多数现有方法需将优化后的高斯分布离线转换为中间隐式场以进行显式网格提取，这阻碍了与下游任务的无缝集成。为解决此局限，我们提出一种新颖的在线框架，通过直接对密集几何高斯表示进行三角剖分来增量式重建和更新高保真显式网格，支持高质量渲染和增量表面重建。我们还提出一种直接网格化算法，能从高斯集合中高效提取和更新网格。为确保网格精度，我们实施基于平面的拉伸约束，使3D高斯基元动态对齐到近似局部表面。此外，我们的框架通过动态冻结完全优化的历史区域，显著减少长序列处理期间的内存和计算开销。在公共数据集上的实验表明，我们的方法在渲染质量和重建精度上均优于传统基于高斯的方法。
英文摘要
Incremental scene reconstruction is essential for real-world applications. Although 3D Gaussian Splatting shows strong potential, most existing approaches require offline conversion of the optimized Gaussians into an intermediate implicit field for explicit mesh extraction, which hinders seamless integration with downstream tasks. To address this limitation, we propose a novel online framework that incrementally reconstructs and updates high-fidelity explicit meshes by directly triangulating a dense geometric Gaussian representation, which supports both high-quality rendering and incremental surface reconstruction. Moreover, we present a direct meshing algorithm that efficiently extracts and updates the mesh from the Gaussian set. To ensure mesh accuracy, we enforce a plane-based pulling constraint that dynamically aligns 3D Gaussian primitives to the approximated local surface. Furthermore, our framework significantly reduces memory and computational overhead during long-sequence processing by dynamically freezing fully optimized historical regions. Experiments on public datasets demonstrate that our method outperforms conventional Gaussian-based methods on both rendering quality and reconstruction accuracy.
