3D Feature Matching
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Benchmarks
3DMatch Benchmark
Most implemented
PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features
3D Point Capsule Networks
EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual Localization
Improving Feature-based Visual Localization by Geometry-Aided Matching
Lepard: Learning partial point cloud matching in rigid and deformable scenes
Papers
AxisPose: Model-Free Matching-Free Single-Shot 6D Object Pose Estimation via Axis Generation
Object pose estimation, which plays a vital role in robotics, augmented reality, and autonomous driving, has been of great interest in computer vision. Existing studies either require multi-stage pose regression or rely …
3D Feature Matching6D Pose Estimation6D Pose Estimation using RGBAutonomous Driving+3PointRecon: Online Point-based 3D Reconstruction via Ray-based 2D-3D Matching
We propose a novel online, point-based 3D reconstruction method from posed monocular RGB videos. Our model maintains a global point cloud representation of the scene, continuously updating the features and 3D locations o…
3D Feature Matching3D ReconstructionRigid Single-Slice-in-Volume registration via rotation-equivariant 2D/3D feature matching
2D to 3D registration is essential in tasks such as diagnosis, surgical navigation, environmental understanding, navigation in robotics, autonomous systems, or augmented reality. In medical imaging, the aim is often to p…
3D Feature MatchingEP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual Localization
Visual localization is the task of estimating a 6-DoF camera pose of a query image within a provided 3D reference map. Thanks to recent advances in various 3D sensors, 3D point clouds are becoming a more accurate and aff…
3D Feature MatchingImage to Point Cloud RegistrationKeypoint DetectionPoint Cloud Registration+2Efficient and Deterministic Search Strategy Based on Residual Projections for Point Cloud Registration with Correspondences
Estimating the rigid transformation between two LiDAR scans through putative 3D correspondences is a typical point cloud registration paradigm. Current 3D feature matching approaches commonly lead to numerous outlier cor…
3D Feature MatchingPoint Cloud RegistrationImproving Feature-based Visual Localization by Geometry-Aided Matching
Feature matching is crucial in visual localization, where 2D-3D correspondence plays a major role in determining the accuracy of camera pose. A sufficient number of well-distributed 2D-3D correspondences is essential for…
3D Feature MatchingPose EstimationVisual Localization