# MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model Selection

arXiv: https://arxiv.org/abs/2505.10874 · HTML: https://mlsanity.com/paper/2505.10874/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 12 of 12)

- [Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm](https://mlsanity.com/paper/1906.02290/) (2019)
- [Multi-Class Model Fitting by Energy Minimization and Mode-Seeking](https://mlsanity.com/paper/1706.00827/) (2017)
- [3D Rigid Motion Segmentation with Mixed and Unknown Number of Models](https://mlsanity.com/paper/1908.06087/) (2019)
- [Motion Segmentation by Exploiting Complementary Geometric Models](https://mlsanity.com/paper/1804.02142/) (2018)
- [Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting](https://mlsanity.com/paper/1802.01129/) (2018)
- [Geometric Multi-Model Fitting with a Convex Relaxation Algorithm](https://mlsanity.com/paper/1706.01553/) (2017)
- [Hypergraph Optimization for Multi-structural Geometric Model Fitting](https://mlsanity.com/paper/2002.05350/) (2020)
- [Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting](https://mlsanity.com/paper/1603.07807/) (2016)
- [Hypergraph Modelling for Geometric Model Fitting](https://mlsanity.com/paper/1607.02829/) (2016)
- [Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting](https://mlsanity.com/paper/1611.01408/) (2016)
- [A Bi-clustering Framework for Consensus Problems](https://mlsanity.com/paper/1405.6159/) (2014)
- [Learning for Multi-Model and Multi-Type Fitting](https://mlsanity.com/paper/1901.10254/) (2019)

## Built on by (most cited first, 2 of 2)

- [Robust Multi-Model Fitting through Learning Neighbor Regions](https://mlsanity.com/paper/2609.15348/) (2026)
- [Superquadric Primitive Decomposition of 3D point clouds via Geometric-Aware Inlier Refinement](https://mlsanity.com/paper/2609.35725/) (2026)

## Abstract (arXiv)

We address the problem of recovering multiple structures of different classes in a dataset contaminated by noise and outliers. In particular, we consider geometric structures defined by a mixture of underlying parametric models (e.g. planes and cylinders, homographies and fundamental matrices), and we tackle the robust fitting problem by preference analysis and clustering. We present a new algorithm, termed MultiLink, that simultaneously deals with multiple classes of models. MultiLink combines on-the-fly model fitting and model selection in a novel linkage scheme that determines whether two clusters are to be merged. The resulting method features many practical advantages with respect to methods based on preference analysis, being faster, less sensitive to the inlier threshold, and able to compensate limitations deriving from hypotheses sampling. Experiments on several public datasets demonstrate that Multi-Link favourably compares with state of the art alternatives, both in multi-class and single-class problems. Code is publicly made available for download.