S2DNet: Learning Accurate Correspondences for Sparse-to-Dense Feature Matching
Establishing robust and accurate correspondences is a fundamental backbone to many computer vision algorithms. While recent learning-based feature matching methods have shown promising results in providing robust correspondences under challenging conditions, they are often limited in terms of precision. In this paper, we introduce S2DNet, a novel feature matching pipeline, designed and trained to efficiently establish both robust and accurate correspondences. By leveraging a sparse-to-dense matching paradigm, we cast the correspondence learning problem as a supervised classification task to learn to output highly peaked correspondence maps. We show that S2DNet achieves state-of-the-art results on the HPatches benchmark, as well as on several long-term visual localization datasets.
Code (4)
Tasks
Visual LocalizationSimilar Papers 제목 키워드 기반
S2DNet: Learning Image Features for Accurate Sparse-to-Dense Matching
Establishing robust and accurate correspondences is a fundamental backbone to many computer vision algorithms. While recent learning-based feature matching methods have shown promising results in providing robust corresp…
Visual LocalizationSAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detection
LiDAR-based 3D object detection plays an essential role in autonomous driving. Existing high-performing 3D object detectors usually build dense feature maps in the backbone network and prediction head. However, the compu…
3D Object DetectionAutonomous DrivingObjectobject-detection+1MV-RoMa: From Pairwise Matching into Multi-View Track Reconstruction
Establishing consistent correspondences across images is essential for 3D vision tasks such as structure-from-motion (SfM), yet most existing matchers operate in a pairwise manner, often producing fragmented and geometri…
Geometry-Aware Feature Matching for Large-Scale Structure from Motion
Establishing consistent and dense correspondences across multiple images is crucial for Structure from Motion (SfM) systems. Significant view changes, such as air-to-ground with very sparse view overlap, pose an even gre…
DKM: Dense Kernelized Feature Matching for Geometry Estimation
Feature matching is a challenging computer vision task that involves finding correspondences between two images of a 3D scene. In this paper we consider the dense approach instead of the more common sparse paradigm, thus…
Camera Pose EstimationGeometric MatchingImage MatchingPose Estimation+1