# Robust Multi-object Matching via Iterative Reweighting of the Graph Connection Laplacian

arXiv: https://arxiv.org/abs/2006.06658 · HTML: https://mlsanity.com/paper/2006.06658/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [Fast, Accurate and Memory-Efficient Partial Permutation Synchronization](https://mlsanity.com/paper/2203.16505/) (2022)
- [Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching](https://mlsanity.com/paper/2201.04797/) (2022)
- [Efficient Detection of Long Consistent Cycles and its Application to Distributed Synchronization](https://mlsanity.com/paper/2407.04260/) (2024)
- [Kernelized multi-graph matching](https://mlsanity.com/paper/2210.05206/) (2022)
- [Tensor-Based Synchronization and the Low-Rankness of the Block Trifocal Tensor](https://mlsanity.com/paper/2409.09313/) (2024)
- [Cycle-Sync: Robust Global Camera Pose Estimation through Enhanced Cycle-Consistent Synchronization](https://mlsanity.com/paper/2511.02329/) (2025)
- [Spectral Robustness for Correlation Clustering Reconstruction in Semi-Adversarial Models](https://mlsanity.com/paper/2108.04729/) (2021)
- [MIXER: Multiattribute, Multiway Fusion of Uncertain Pairwise Affinities](https://mlsanity.com/paper/2210.08360/) (2022)
- [Population-wise Labeling of Sulcal Graphs using Multi-graph Matching](https://mlsanity.com/paper/2301.13532/) (2023)
- [Recovery Theory for Projected Power Iterations in Permutation Synchronization](https://mlsanity.com/paper/2609.09502/) (2026)

## Abstract (arXiv)

We propose an efficient and robust iterative solution to the multi-object matching problem. We first clarify serious limitations of current methods as well as the inappropriateness of the standard iteratively reweighted least squares procedure. In view of these limitations, we suggest a novel and more reliable iterative reweighting strategy that incorporates information from higher-order neighborhoods by exploiting the graph connection Laplacian. We demonstrate the superior performance of our procedure over state-of-the-art methods using both synthetic and real datasets.