arXiv：cs.LG· Yuzhou Cheng, Tom Yates, Ignacio Alzugaray, Danyal Akarca, Pedro A. M. Mediano, Andrew J. Davison·· 4 小时前AI 评分37
Hierarchy-GBP：通过抽象与恢复加速因子图推理
Hierarchy-GBP: Accelerating Factor Graph Inference via Abstraction and Recovery
AI 导读
研究者提出 Hierarchy-GBP（H-GBP），一种两阶段迭代框架，通过粗图近似（抽象）先解决全局误差、再投影回原图（恢复），最后用 GBP 精修局部误差。
正文
Abstract:Gaussian Belief Propagation (GBP) is a distributed inference algorithm that passes messages in graphical models, making it attractive for scalable spatial intelligence. However, we find GBP most effective locally: it rapidly smooths message errors that vary sharply between neighbor variables, but corrects global errors across distant graph regions incrementally through long-range message propagations. We propose Hierarchy-GBP (H-GBP), an iterative, two-stage framework that accelerates GBP by first solving these global errors with a coarse graph approximation (abstraction) and projecting the results back to the original graph (recovery), then refining the remaining local errors with GBP. We prove H-GBP convergence to the optimum by deriving the combined matrix operator of our abstraction and recovery steps and analyzing its spectral radius. Experiments on linear sparse graphs show that H-GBP converges fundamentally faster than standard GBP. Moreover, we validate H-GBP on two important spatial problems: Pose Graph Optimization (PGO) and Bundle Adjustment (BA). H-GBP markedly accelerates large-scale PGO and achieves state-of-the-art runtime across all tested BA scales.
| Comments: | 33 pages, 10 figures, including appendices |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO) |
| Cite as: | arXiv:2610.06978 [cs.CV] |
| (or arXiv:2610.06978v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.06978 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuzhou Cheng [view email]
[v1]
Sun, 4 Oct 2026 00:50:25 UTC (5,222 KB)
来源：arXiv：cs.LG · arxiv.org