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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

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Showing 201–225 of 339 papers

TitleStatusHype
HPRNet: Hierarchical Point Regression for Whole-Body Human Pose EstimationCode1
RegionViT: Regional-to-Local Attention for Vision TransformersCode1
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud RegistrationCode1
DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single CameraCode1
MarkerPose: Robust Real-time Planar Target Tracking for Accurate Stereo Pose EstimationCode1
HoughNet: Integrating near and long-range evidence for visual detectionCode1
Pose Recognition with Cascade TransformersCode1
DexYCB: A Benchmark for Capturing Hand Grasping of ObjectsCode1
Unsupervised Visual Attention and Invariance for Reinforcement Learning—0
Learning Spatial Context with Graph Neural Network for Multi-Person Pose GroupingCode0
Bottom-Up Human Pose Estimation Via Disentangled Keypoint RegressionCode1
Unconstrained Face Recognition using ASURF and Cloud-Forest Classifier optimized with VLAD—0
End-to-end learning of keypoint detection and matching for relative pose estimation—0
LatentKeypointGAN: Controlling Images via Latent KeypointsCode0
3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARsCode1
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types—0
End-to-End Trainable Multi-Instance Pose Estimation with TransformersCode1
Deep Dual Consecutive Network for Human Pose EstimationCode1
Regressive Domain Adaptation for Unsupervised Keypoint DetectionCode0
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal AssociationCode2
FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images—0
Multi-Instance Pose Networks: Rethinking Top-Down Pose EstimationCode1
Semi-supervised Keypoint Localization—0
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos—0
Robust Automatic Monocular Vehicle Speed Estimation for Traffic Surveillance—0
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