# BCLNet: Bilateral Consensus Learning for Two-View Correspondence Pruning

arXiv: https://arxiv.org/abs/2401.03459 · HTML: https://mlsanity.com/paper/2401.03459/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 7 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Image Matching across Wide Baselines: From Paper to Practice](https://mlsanity.com/paper/2003.01587/) (2020)
- [Learning to Find Good Correspondences](https://mlsanity.com/paper/1711.05971/) (2017)
- [Learning Two-View Correspondences and Geometry Using Order-Aware Network](https://mlsanity.com/paper/1908.04964/) (2019)
- [Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses](https://mlsanity.com/paper/1905.04132/) (2019)
- [Progressive Correspondence Pruning by Consensus Learning](https://mlsanity.com/paper/2101.00591/) (2021)
- [A Decomposition Model for Stereo Matching](https://mlsanity.com/paper/2104.07516/) (2021)

## Built on by (most cited first, 12 of 12)

- [Deep Learning Reforms Image Matching: A Survey and Outlook](https://mlsanity.com/paper/2506.04619/) (2025)
- [Latent Semantic Consensus For Deterministic Geometric Model Fitting](https://mlsanity.com/paper/2403.06444/) (2024)
- [Scalable and Generalizable Correspondence Pruning via Geometry-Consistent Pre-training](https://mlsanity.com/paper/2406.05773/) (2024)
- [MGCA-Net: Multi-Graph Contextual Attention Network for Two-View Correspondence Learning](https://mlsanity.com/paper/2512.23369/) (2025)
- [Consensus Learning with Deep Sets for Essential Matrix Estimation](https://mlsanity.com/paper/2406.17414/) (2024)
- [Selecting and Pruning: A Differentiable Causal Sequentialized State-Space Model for Two-View Correspondence Learning](https://mlsanity.com/paper/2503.17938/) (2025)
- [CorrMoE: Mixture of Experts with De-stylization Learning for Cross-Scene and Cross-Domain Correspondence Pruning](https://mlsanity.com/paper/2507.11834/) (2025)
- [GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry](https://mlsanity.com/paper/2508.00592/) (2025)
- [LeCoT: revisiting network architecture for two-view correspondence pruning](https://mlsanity.com/paper/2511.07078/) (2025)
- [SC-Net: Robust Correspondence Learning via Spatial and Cross-Channel Context](https://mlsanity.com/paper/2512.23473/) (2025)
- [SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning](https://mlsanity.com/paper/2606.04493/) (2026)
- [See More, Match Better: Multi-Source Feature Fusion for Two-View Correspondence Learning](https://mlsanity.com/paper/2606.09262/) (2026)

## Abstract (arXiv)

Correspondence pruning aims to establish reliable correspondences between two related images and recover relative camera motion. Existing approaches often employ a progressive strategy to handle the local and global contexts, with a prominent emphasis on transitioning from local to global, resulting in the neglect of interactions between different contexts. To tackle this issue, we propose a parallel context learning strategy that involves acquiring bilateral consensus for the two-view correspondence pruning task. In our approach, we design a distinctive self-attention block to capture global context and parallel process it with the established local context learning module, which enables us to simultaneously capture both local and global consensuses. By combining these local and global consensuses, we derive the required bilateral consensus. We also design a recalibration block, reducing the influence of erroneous consensus information and enhancing the robustness of the model. The culmination of our efforts is the Bilateral Consensus Learning Network (BCLNet), which efficiently estimates camera pose and identifies inliers (true correspondences). Extensive experiments results demonstrate that our network not only surpasses state-of-the-art methods on benchmark datasets but also showcases robust generalization abilities across various feature extraction techniques. Noteworthily, BCLNet obtains 3.98% mAP5circ gains over the second best method on unknown outdoor dataset, and obviously accelerates model training speed. The source code will be available at: https://github.com/guobaoxiao/BCLNet.