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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 1120 of 339 papers

TitleStatusHype
X-Pose: Detecting Any KeypointsCode2
InstructDiffusion: A Generalist Modeling Interface for Vision TasksCode2
DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature MatchingCode2
Detector-Free Structure from MotionCode2
SiLK -- Simple Learned KeypointsCode2
SNAKE: Shape-aware Neural 3D Keypoint FieldCode2
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal AssociationCode2
Objects as PointsCode2
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity FieldsCode2
TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor LearningCode1
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