通过骨架锚定分层重建，解决三维重建中重复结构导致的歧义问题。
HiSfM: Disambiguating Structure-from-Motion via Scaffold-Anchored Hierarchical Reconstruction
- 用几何启发式构建局部群组，再通过无边相交生成树连接成紧凑骨架
- 在基准测试中减少计算耗时，同时避免因结构对称引发的重建失败
- 适合需要高鲁棒性与效率的机器人建图和大规模场景建模任务
Structure-from-Motion (SfM) 是支撑机器人导航、定位与大场景建模的重要稀疏三维重建工具。然而，传统方法在重复或对称结构带来的视觉歧义下容易失效，且因冗余相机和约束导致计算开销大。本文提出 HiSfM，一种分层粗到细的 SfM 框架，通过构建骨架提升鲁棒性与效率。首先利用几何启发式形成强局部社区，再通过打包边不相交生成树（EDST）构建紧凑而稳固的骨架，并以两视图消歧器验证骨架边。在此验证骨架上重建稳定骨架作为场景锚点，随后高效注册并三角化剩余图像进行精修。在聚焦歧义的基准与通用数据集上的实验表明，HiSfM 有效防止歧义导致的失败，显著降低运行时间，同时比激进稀疏化方法更具完整性。代码已开源：https://github.com/3dv-casia/HiSfM。
原文摘要 · Abstract (English)
Structure-from-Motion (SfM) is a fundamental tool for sparse 3D reconstruction with broad impact in robotics and vision, supporting mapping, localization, and large-scale scene modeling. However, conventional pipelines often fail under hard visual ambiguity caused by repeated or symmetric structures, and incur heavy computational cost due to redundant cameras and constraints. We present HiSfM, a hierarchical coarse-to-fine SfM framework that improves robustness and efficiency through scaffold construction. HiSfM first forms strong local communities using geometrical induced heuristics, then connects communities with a compact yet strong skeleton by packing edge-disjoint spanning trees (EDST) while verifying skeletal edges with a two-view disambiguator. We reconstruct a stable scaffold on this verified skeleton, serving as an anchor to capture the essence of the scene, and subsequently absorb remaining images via efficient registration and triangulation for further refinements. Experiments on ambiguity-focused benchmarks and general datasets show that HiSfM prevents ambiguity-induced failures while substantially reducing runtime compared to previous methods, and improves completeness over aggressive sparsification methods. Code is available at https://github.com/3dv-casia/HiSfM.
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