# Latent Semantic Consensus For Deterministic Geometric Model Fitting

arXiv: https://arxiv.org/abs/2403.06444 · HTML: https://mlsanity.com/paper/2403.06444/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 67 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 15 of 15)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Learning Two-View Correspondences and Geometry Using Order-Aware Network](https://mlsanity.com/paper/1908.04964/) (2019)
- [Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion](https://mlsanity.com/paper/2006.08159/) (2020)
- [Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm](https://mlsanity.com/paper/1906.02290/) (2019)
- [Adaptive Data-Free Quantization](https://mlsanity.com/paper/2303.06869/) (2023)
- [Kernelized Multiview Subspace Analysis by Self-weighted Learning](https://mlsanity.com/paper/1911.10357/) (2019)
- [A Hybrid Quantum-Classical Algorithm for Robust Fitting](https://mlsanity.com/paper/2201.10110/) (2022)
- [BCLNet: Bilateral Consensus Learning for Two-View Correspondence Pruning](https://mlsanity.com/paper/2401.03459/) (2024)
- [Deterministic Approximate Methods for Maximum Consensus Robust Fitting](https://mlsanity.com/paper/1710.10003/) (2017)
- [3D Rigid Motion Segmentation with Mixed and Unknown Number of Models](https://mlsanity.com/paper/1908.06087/) (2019)
- [Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference](https://mlsanity.com/paper/2006.08804/) (2020)
- [Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data](https://mlsanity.com/paper/1509.02649/) (2015)
- [Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting](https://mlsanity.com/paper/1802.01129/) (2018)
- [Graph Context Transformation Learning for Progressive Correspondence Pruning](https://mlsanity.com/paper/2312.15971/) (2023)
- [Effective Sampling: Fast Segmentation Using Robust Geometric Model Fitting](https://mlsanity.com/paper/1705.09437/) (2017)

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

- [LLHA-Net: A Hierarchical Attention Network for Two-View Correspondence Learning](https://mlsanity.com/paper/2512.24620/) (2025)
- [Multi-instance robust fitting for non-classical geometric models](https://mlsanity.com/paper/2602.05602/) (2026)

## Abstract (arXiv)

Estimating reliable geometric model parameters from the data with severe outliers is a fundamental and important task in computer vision. This paper attempts to sample high-quality subsets and select model instances to estimate parameters in the multi-structural data. To address this, we propose an effective method called Latent Semantic Consensus (LSC). The principle of LSC is to preserve the latent semantic consensus in both data points and model hypotheses. Specifically, LSC formulates the model fitting problem into two latent semantic spaces based on data points and model hypotheses, respectively. Then, LSC explores the distributions of points in the two latent semantic spaces, to remove outliers, generate high-quality model hypotheses, and effectively estimate model instances. Finally, LSC is able to provide consistent and reliable solutions within only a few milliseconds for general multi-structural model fitting, due to its deterministic fitting nature and efficiency. Compared with several state-of-the-art model fitting methods, our LSC achieves significant superiority for the performance of both accuracy and speed on synthetic data and real images. The code will be available at https://github.com/guobaoxiao/LSC.