Hierarchy-GBP: Accelerating Factor Graph Inference via Abstraction and Recovery
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. W...
| Authors | Yuzhou Cheng, Tom Yates, Ignacio Alzugaray et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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