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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 131–140 of 339 papers

TitleStatusHype
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
ViTPose++: Vision Transformer for Generic Body Pose EstimationCode3
DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic ModelCode0
Images Speak in Images: A Generalist Painter for In-Context Visual LearningCode4
Dense Interspecies Face EmbeddingCode1
BALF: Simple and Efficient Blur Aware Local Feature Detector—0
Real-time Detection of 2D Tool Landmarks with Synthetic Training Data—0
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