{"total":67,"rows":[{"id":"2406.09756","title":"Grounding Image Matching in 3D with MASt3R","created":"2024-06-14","cited_by_count":983,"area":null},{"id":"2305.15404","title":"RoMa: Robust Dense Feature Matching","created":"2023-05-24","cited_by_count":302,"area":null},{"id":"2103.01486","title":"Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition","created":"2021-03-02","cited_by_count":238,"area":null},{"id":"2103.14167","title":"COTR: Correspondence Transformer for Matching Across Images","created":"2021-03-25","cited_by_count":183,"area":null},{"id":"2202.00667","title":"DKM: Dense Kernelized Feature Matching for Geometry Estimation","created":"2022-02-01","cited_by_count":177,"area":null},{"id":"2101.01710","title":"Learning Accurate Dense Correspondences and When to Trust Them","created":"2021-01-05","cited_by_count":113,"area":null},{"id":"2109.13912","title":"PDC-Net+: Enhanced Probabilistic Dense Correspondence Network","created":"2021-09-28","cited_by_count":80,"area":null},{"id":"2407.15420","title":"Local All-Pair Correspondence for Point Tracking","created":"2024-07-22","cited_by_count":80,"area":null},{"id":"2106.02520","title":"CATs: Cost Aggregation Transformers for Visual Correspondence","created":"2021-06-04","cited_by_count":77,"area":null},{"id":"2202.06817","title":"CATs++: Boosting Cost Aggregation with Convolutions and Transformers","created":"2022-02-14","cited_by_count":53,"area":null},{"id":"2507.16290","title":"Dens3R: A Foundation Model for 3D Geometry Prediction","created":"2025-07-22","cited_by_count":46,"area":null},{"id":"2104.03308","title":"Warp Consistency for Unsupervised Learning of Dense Correspondences","created":"2021-04-07","cited_by_count":42,"area":null},{"id":"2506.09278","title":"UFM: A Simple Path towards Unified Dense Correspondence with Flow","created":"2025-06-10","cited_by_count":36,"area":null},{"id":"2108.00211","title":"Multi-scale Matching Networks for Semantic Correspondence","created":"2021-07-31","cited_by_count":32,"area":null},{"id":"2210.02689","title":"Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence","created":"2022-10-06","cited_by_count":32,"area":null},{"id":"2112.03243","title":"Input-level Inductive Biases for 3D Reconstruction","created":"2021-12-06","cited_by_count":31,"area":null},{"id":"2207.06825","title":"Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions","created":"2022-07-14","cited_by_count":31,"area":null},{"id":"2106.03090","title":"Deep Matching Prior: Test-Time Optimization for Dense Correspondence","created":"2021-06-06","cited_by_count":30,"area":null},{"id":"2203.04279","title":"Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences","created":"2022-03-08","cited_by_count":30,"area":null},{"id":"2208.02879","title":"PointConvFormer: Revenge of the Point-based Convolution","created":"2022-08-04","cited_by_count":30,"area":null},{"id":"2412.09072","title":"Cross-View Completion Models are Zero-shot Correspondence Estimators","created":"2024-12-12","cited_by_count":30,"area":null},{"id":"2303.17342","title":"PMatch: Paired Masked Image Modeling for Dense Geometric Matching","created":"2023-03-30","cited_by_count":29,"area":null},{"id":"2304.14845","title":"SFD2: Semantic-guided Feature Detection and Description","created":"2023-04-28","cited_by_count":28,"area":null},{"id":"2401.17592","title":"Local Feature Matching Using Deep Learning: A Survey","created":"2024-01-31","cited_by_count":26,"area":null},{"id":"2305.19094","title":"Diffusion Model for Dense Matching","created":"2023-05-30","cited_by_count":21,"area":"diffusion"},{"id":"2209.12213","title":"ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement","created":"2022-09-25","cited_by_count":18,"area":null},{"id":"2308.11165","title":"Improving Misaligned Multi-modality Image Fusion with One-stage Progressive Dense Registration","created":"2023-08-22","cited_by_count":17,"area":null},{"id":"2107.00771","title":"Overcoming Obstructions via Bandwidth-Limited Multi-Agent Spatial Handshaking","created":"2021-07-01","cited_by_count":16,"area":null},{"id":"2203.16038","title":"Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels","created":"2022-03-30","cited_by_count":16,"area":null},{"id":"2401.16741","title":"MESA: Matching Everything by Segmenting Anything","created":"2024-01-30","cited_by_count":16,"area":null},{"id":"2110.15904","title":"Learning Co-segmentation by Segment Swapping for Retrieval and Discovery","created":"2021-10-29","cited_by_count":15,"area":null},{"id":"2312.06642","title":"CorresNeRF: Image Correspondence Priors for Neural Radiance Fields","created":"2023-12-11","cited_by_count":15,"area":null},{"id":"2308.14039","title":"Deep Learning for Visual Localization and Mapping: A Survey","created":"2023-08-27","cited_by_count":14,"area":null},{"id":"2403.11120","title":"Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence","created":"2024-03-17","cited_by_count":14,"area":null},{"id":"2112.04846","title":"ScaleNet: A Shallow Architecture for Scale Estimation","created":"2021-12-09","cited_by_count":12,"area":null},{"id":"2407.18907","title":"SHIC: Shape-Image Correspondences with no Keypoint Supervision","created":"2024-07-26","cited_by_count":12,"area":null},{"id":"2208.00150","title":"Learning Shadow Correspondence for Video Shadow Detection","created":"2022-07-30","cited_by_count":9,"area":null},{"id":"2112.06910","title":"DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points","created":"2021-12-13","cited_by_count":8,"area":null},{"id":"2201.05816","title":"A Critical Analysis of Image-based Camera Pose Estimation Techniques","created":"2022-01-15","cited_by_count":7,"area":null},{"id":"2502.20685","title":"EDM: Equirectangular Projection-Oriented Dense Kernelized Feature Matching","created":"2025-02-28","cited_by_count":7,"area":null},{"id":"2506.04619","title":"Deep Learning Reforms Image Matching: A Survey and Outlook","created":"2025-06-05","cited_by_count":7,"area":null},{"id":"2104.03097","title":"LIFE: Lighting Invariant Flow Estimation","created":"2021-04-07","cited_by_count":6,"area":null},{"id":"2209.08742","title":"Integrative Feature and Cost Aggregation with Transformers for Dense Correspondence","created":"2022-09-19","cited_by_count":6,"area":null},{"id":"2209.08896","title":"NeuralMarker: A Framework for Learning General Marker Correspondence","created":"2022-09-19","cited_by_count":4,"area":null},{"id":"2110.04773","title":"Digging Into Self-Supervised Learning of Feature Descriptors","created":"2021-10-10","cited_by_count":3,"area":null},{"id":"2306.04231","title":"Learning Probabilistic Coordinate Fields for Robust Correspondences","created":"2023-06-07","cited_by_count":3,"area":null},{"id":"2306.05749","title":"DocAligner: Annotating Real-world Photographic Document Images by Simply Taking Pictures","created":"2023-06-09","cited_by_count":3,"area":null},{"id":"2501.03220","title":"ProTracker: Probabilistic Integration for Robust and Accurate Point Tracking","created":"2025-01-06","cited_by_count":3,"area":null},{"id":"2502.12545","title":"IM360: Large-scale Indoor Mapping with 360 Cameras","created":"2025-02-18","cited_by_count":3,"area":null},{"id":"2311.18540","title":"Match me if you can: Semi-Supervised Semantic Correspondence Learning with Unpaired Images","created":"2023-11-30","cited_by_count":2,"area":null},{"id":"2404.02678","title":"Independently Keypoint Learning for Small Object Semantic Correspondence","created":"2024-04-03","cited_by_count":2,"area":null},{"id":"2201.03212","title":"Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition","created":"2022-01-10","cited_by_count":1,"area":null},{"id":"2312.15970","title":"Learning Deformable Hypothesis Sampling for Accurate PatchMatch Multi-View Stereo","created":"2023-12-26","cited_by_count":1,"area":null},{"id":"2503.17715","title":"Normalized Matching Transformer","created":"2025-03-22","cited_by_count":1,"area":null},{"id":"2504.12869","title":"SC3EF: A Joint Self-Correlation and Cross-Correspondence Estimation Framework for Visible and Thermal Image Registration","created":"2025-04-17","cited_by_count":1,"area":null},{"id":"2603.04869","title":"SURE: Semi-dense Uncertainty-REfined Feature Matching","created":"2026-03-05","cited_by_count":1,"area":null},{"id":"2107.00769","title":"Enhancing Multi-Robot Perception via Learned Data Association","created":"2021-07-01","cited_by_count":0,"area":null},{"id":"2201.12828","title":"Comprehensive Saliency Fusion for Object Co-segmentation","created":"2022-01-30","cited_by_count":0,"area":null},{"id":"2204.02164","title":"Joint Learning of Feature Extraction and Cost Aggregation for Semantic Correspondence","created":"2022-04-05","cited_by_count":0,"area":null},{"id":"2212.10174","title":"CGCV:Context Guided Correlation Volume for Optical Flow Neural Networks","created":"2022-12-20","cited_by_count":0,"area":null},{"id":"2511.13168","title":"SOMA: Feature Gradient Enhanced Affine-Flow Matching for SAR-Optical Registration","created":"2025-11-17","cited_by_count":0,"area":null},{"id":"2602.02154","title":"Deep learning enables urban change profiling through alignment of historical maps","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2603.14176","title":"BluRef: Unsupervised Image Deblurring with Dense-Matching References","created":"2026-03-15","cited_by_count":0,"area":null},{"id":"2604.06713","title":"Improving Local Feature Matching by Entropy-inspired Scale Adaptability and Flow-endowed Local Consistency","created":"2026-04-08","cited_by_count":0,"area":null},{"id":"2605.22069","title":"TWINGS: Thin Plate Splines Warp-aligned Initialization for Sparse-View Gaussian Splatting","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2608.11093","title":"Cross-View Feature Matching: Survey, Benchmarking, and Foundation-Model Perspectives","created":"2026-08-11","cited_by_count":0,"area":null},{"id":"2609.26161","title":"Moving6DPoSe: A Multimodal Database for Monocular 6D Pose Estimation and Segmentation of Moving Objects","created":"2026-08-09","cited_by_count":0,"area":null}],"next_cursor":null}