mlsanityData to 14 Oct 2026

Neighbourhood Consensus Networks

arXiv 1810.10510 ↗First posted 24 Oct 2018cs.CV cs.LGcited by 174

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Abstract (arXiv)

We address the problem of finding reliable dense correspondences between a pair of images. This is a challenging task due to strong appearance differences between the corresponding scene elements and ambiguities generated by repetitive patterns. The contributions of this work are threefold. First, inspired by the classic idea of disambiguating feature matches using semi-local constraints, we develop an end-to-end trainable convolutional neural network architecture that identifies sets of spatially consistent matches by analyzing neighbourhood consensus patterns in the 4D space of all possible correspondences between a pair of images without the need for a global geometric model. Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences. Third, we show the proposed neighbourhood consensus network can be applied to a range of matching tasks including both category- and instance-level matching, obtaining the state-of-the-art results on the PF Pascal dataset and the InLoc indoor visual localization benchmark.

Key facts. Cited by 174 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Oct 2018.

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Citing papers per year: 2021: 32; 2022: 46; 2023: 45; 2024: 20; 2025: 24; 2026: 7.

174 papers.

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  1. 2021LoFTR: Detector-Free Local Feature Matching with Transformerscited by 984
  2. 2023Emergent Correspondence from Image Diffusioncited by 446
  3. 2023RoMa: Robust Dense Feature Matchingcited by 302
  4. 2022Geometric Transformer for Fast and Robust Point Cloud Registrationcited by 202
  5. 2021Pixel-Perfect Structure-from-Motion with Featuremetric Refinementcited by 200
  6. 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
  7. 2023Tracking Everything Everywhere All at Oncecited by 193
  8. 2021Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenescited by 190
  9. 2021COTR: Correspondence Transformer for Matching Across Imagescited by 183
  10. 2022DKM: Dense Kernelized Feature Matching for Geometry Estimationcited by 177
  11. 2022ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformercited by 171
  12. 2023Optimal Transport Aggregation for Visual Place Recognitioncited by 152
38 more
  1. 2022AiATrack: Attention in Attention for Transformer Visual Trackingcited by 137
  2. 2023Cross-Image Attention for Zero-Shot Appearance Transfercited by 136
  3. 2024Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speedcited by 120
  4. 2021LASR: Learning Articulated Shape Reconstruction from a Monocular Videocited by 116
  5. 2022Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentationcited by 114
  6. 2021Learning Accurate Dense Correspondences and When to Trust Themcited by 113
  7. 2022ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wildcited by 92
  8. 2021Lepard: Learning partial point cloud matching in rigid and deformable scenescited by 85
  9. 2023Hierarchical Dense Correlation Distillation for Few-Shot Segmentationcited by 84
  10. 2023GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformercited by 84
  11. 2022FS6D: Few-Shot 6D Pose Estimation of Novel Objectscited by 81
  12. 2021PDC-Net+: Enhanced Probabilistic Dense Correspondence Networkcited by 80
  13. 2021CATs: Cost Aggregation Transformers for Visual Correspondencecited by 77
  14. 2021Relational Embedding for Few-Shot Classificationcited by 74
  15. 2023Detector-Free Structure from Motioncited by 71
  16. 2023Rotation-Invariant Transformer for Point Cloud Matchingcited by 69
  17. 2023GlueStick: Robust Image Matching by Sticking Points and Lines Togethercited by 66
  18. 2022Global Matching with Overlapping Attention for Optical Flow Estimationcited by 64
  19. 2021GAN-Supervised Dense Visual Alignmentcited by 62
  20. 2022ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matchingcited by 62
  21. 2022MeshLoc: Mesh-Based Visual Localizationcited by 62
  22. 2021DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervisioncited by 59
  23. 2022CATs++: Boosting Cost Aggregation with Convolutions and Transformerscited by 53
  24. 2021HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registrationcited by 48
  25. 2024XoFTR: Cross-modal Feature Matching Transformercited by 48
  26. 2022Correlation Verification for Image Retrievalcited by 47
  27. 2021Convolutional Hough Matching Networkscited by 43
  28. 2021Warp Consistency for Unsupervised Learning of Dense Correspondencescited by 42
  29. 2025MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Trainingcited by 41
  30. 2021Planar Surface Reconstruction from Sparse Viewscited by 40
  31. 2022Visual correspondence-based explanations improve AI robustness and human-AI team accuracycited by 39
  32. 2023SD4Match: Learning to Prompt Stable Diffusion Model for Semantic Matchingcited by 38
  33. 2023Improving Semantic Correspondence with Viewpoint-Guided Spherical Mapscited by 34
  34. 2023Rethinking Optical Flow from Geometric Matching Consistent Perspectivecited by 33
  35. 2021Multi-scale Matching Networks for Semantic Correspondencecited by 32
  36. 2022TopicFM: Robust and Interpretable Topic-Assisted Feature Matchingcited by 32
  37. 2022Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondencecited by 32
  38. 2021Input-level Inductive Biases for 3D Reconstructioncited by 31

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