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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 201–210 of 339 papers

TitleStatusHype
Towards High Performance One-Stage Human Pose EstimationCode0
NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action—0
SiLK: Simple Learned Keypoints—0
Cross-Domain 3D Hand Pose Estimation With Dual Modalities—0
HandsOff: Labeled Dataset Generation With No Additional Human Annotations—0
Learning Markerless Robot-Depth Camera Calibration and End-Effector Pose Estimation—0
Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB ImagesCode0
DDM-NET: End-to-end learning of keypoint feature Detection, Description and Matching for 3D localizationCode0
DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic ModelCode0
BALF: Simple and Efficient Blur Aware Local Feature Detector—0
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