# Learnable Motion Coherence for Correspondence Pruning

arXiv: https://arxiv.org/abs/2011.14563 · HTML: https://mlsanity.com/paper/2011.14563/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 20 of 20)

- [PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction](https://mlsanity.com/paper/2311.12024/) (2023)
- [SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration](https://mlsanity.com/paper/2203.14453/) (2022)
- [Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning](https://mlsanity.com/paper/2102.03214/) (2021)
- [DetarNet: Decoupling Translation and Rotation by Siamese Network for Point Cloud Registration](https://mlsanity.com/paper/2112.14059/) (2021)
- [SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning](https://mlsanity.com/paper/2111.14345/) (2021)
- [IMP: Iterative Matching and Pose Estimation with Adaptive Pooling](https://mlsanity.com/paper/2304.14837/) (2023)
- [MGNet: Learning Correspondences via Multiple Graphs](https://mlsanity.com/paper/2401.04984/) (2024)
- [VSFormer: Visual-Spatial Fusion Transformer for Correspondence Pruning](https://mlsanity.com/paper/2312.08774/) (2023)
- [Deep Learning Reforms Image Matching: A Survey and Outlook](https://mlsanity.com/paper/2506.04619/) (2025)
- [Learning Second-Order Attentive Context for Efficient Correspondence Pruning](https://mlsanity.com/paper/2303.15761/) (2023)
- [Learning Feature Matching via Matchable Keypoint-Assisted Graph Neural Network](https://mlsanity.com/paper/2307.01447/) (2023)
- [Scalable and Generalizable Correspondence Pruning via Geometry-Consistent Pre-training](https://mlsanity.com/paper/2406.05773/) (2024)
- [ResMatch: Residual Attention Learning for Local Feature Matching](https://mlsanity.com/paper/2307.05180/) (2023)
- [Consensus Learning with Deep Sets for Essential Matrix Estimation](https://mlsanity.com/paper/2406.17414/) (2024)
- [CorrAdaptor: Adaptive Local Context Learning for Correspondence Pruning](https://mlsanity.com/paper/2408.08134/) (2024)
- [Selecting and Pruning: A Differentiable Causal Sequentialized State-Space Model for Two-View Correspondence Learning](https://mlsanity.com/paper/2503.17938/) (2025)
- [Geometry Depth Consistency in RGBD Relative Pose Estimation](https://mlsanity.com/paper/2401.00639/) (2024)
- [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)
- [LLHA-Net: A Hierarchical Attention Network for Two-View Correspondence Learning](https://mlsanity.com/paper/2512.24620/) (2025)

## Abstract (arXiv)

Motion coherence is an important clue for distinguishing true correspondences from false ones. Modeling motion coherence on sparse putative correspondences is challenging due to their sparsity and uneven distributions. Existing works on motion coherence are sensitive to parameter settings and have difficulty in dealing with complex motion patterns. In this paper, we introduce a network called Laplacian Motion Coherence Network (LMCNet) to learn motion coherence property for correspondence pruning. We propose a novel formulation of fitting coherent motions with a smooth function on a graph of correspondences and show that this formulation allows a closed-form solution by graph Laplacian. This closed-form solution enables us to design a differentiable layer in a learning framework to capture global motion coherence from putative correspondences. The global motion coherence is further combined with local coherence extracted by another local layer to robustly detect inlier correspondences. Experiments demonstrate that LMCNet has superior performances to the state of the art in relative camera pose estimation and correspondences pruning of dynamic scenes.