SOTAVerified

Keypoint Detection

Keypoint Detection is essential for analyzing and interpreting images in computer vision. It involves simultaneously detecting and localizing interesting points in an image. Keypoints, also known as interest points, are spatial locations or points in the image that define what is interesting or what stands out. They are invariant to image rotation, shrinkage, translation, distortion, etc. Keypoints examples are body joints, facial landmarks, or any other salient points in objects. Keypoints have uses in problems such as pose estimation, object detection and tracking, facial analysis, and augmented reality.

( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 111–120 of 339 papers

TitleStatusHype
Self-supervised Interest Point Detection and Description for Fisheye and Perspective Images—0
Human Pose Estimation in Monocular Omnidirectional Top-View Images—0
SiLK -- Simple Learned KeypointsCode2
From Saliency to DINO: Saliency-guided Vision Transformer for Few-shot Keypoint Detection—0
NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point CloudCode1
Semantic Image Attack for Visual Model Diagnosis—0
Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty PropagationCode1
ShaRPy: Shape Reconstruction and Hand Pose Estimation from RGB-D with Uncertainty—0
KGNv2: Separating Scale and Pose Prediction for Keypoint-based 6-DoF Grasp Synthesis on RGB-D inputCode1
PaRK-Detect: Towards Efficient Multi-Task Satellite Imagery Road Extraction via Patch-Wise Keypoints Detection—0
Show:102550
← PrevPage 12 of 34Next →

No leaderboard results yet.