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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

Showing 141150 of 339 papers

TitleStatusHype
DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic ModelCode0
6-DoF Object Pose from Semantic KeypointsCode0
MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual NetworkCode0
Multiview Supervision By RegistrationCode0
Multi-Person Pose Estimation with Local Joint-to-Person AssociationsCode0
Neural Outlier Rejection for Self-Supervised Keypoint LearningCode0
MK-Pose: Category-Level Object Pose Estimation via Multimodal-Based Keypoint LearningCode0
MONET: Multiview Semi-supervised Keypoint Detection via Epipolar DivergenceCode0
Keypoint-based Stereophotoclinometry for Characterizing and Navigating Small Bodies: A Factor Graph ApproachCode0
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation ModelCode0
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