无需简化步骤，直接从多视角图像重建轻量级建筑模型
SF-Recon: Simplification-Free Lightweight Building Reconstruction via 3D Gaussian Splatting
- 用3D高斯点云直接构建视图一致的建筑表面表示
- 通过法向梯度引导优化，保留屋顶墙角结构，减少90%以上面数
- 适合城市数字孪生、导航系统等需要快速建模的场景
轻量级建筑表面模型对数字城市、导航和快速地理空间分析至关重要。传统多视图几何方法因依赖密集重建、网格化和后续简化，流程繁琐且对质量敏感。本文提出SF-Recon，直接从多视角图像重建轻量建筑表面，无需后处理简化。首先训练初始3D高斯点云场，获得视图一致性表示；再通过法向梯度引导的高斯优化，选择与屋顶和墙面边界对齐的点元；随后采用多视角边缘一致性剪枝，增强结构锐度并抑制非结构伪影，全程无外部监督；最后通过多视角深度约束的Delaunay三角剖分，将结构化高斯场转为轻量、结构忠实的建筑网格。基于自建的SF数据集，实验表明，该方法可直接生成轻量建筑模型，面数和顶点数显著减少，同时保持计算效率。
原文摘要 · Abstract (English)
Lightweight building surface models are crucial for digital city, navigation, and fast geospatial analytics, yet conventional multi-view geometry pipelines remain cumbersome and quality-sensitive due to their reliance on dense reconstruction, meshing, and subsequent simplification. This work presents SF-Recon, a method that directly reconstructs lightweight building surfaces from multi-view images without post-hoc mesh simplification. We first train an initial 3D Gaussian Splatting (3DGS) field to obtain a view-consistent representation. Building structure is then distilled by a normal-gradient-guided Gaussian optimization that selects primitives aligned with roof and wall boundaries, followed by multi-view edge-consistency pruning to enhance structural sharpness and suppress non-structural artifacts without external supervision. Finally, a multi-view depth-constrained Delaunay triangulation converts the structured Gaussian field into a lightweight, structurally faithful building mesh. Based on a proposed SF dataset, the experimental results demonstrate that our SF-Recon can directly reconstruct lightweight building models from multi-view imagery, achieving substantially fewer faces and vertices while maintaining computational efficiency. Website:https://lzh282140127-cell.github.io/SF-Recon-project/
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