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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 91100 of 339 papers

TitleStatusHype
Self-supervised Learning of Contextualized Local Visual EmbeddingsCode0
RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy0
EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual LocalizationCode1
InstructDiffusion: A Generalist Modeling Interface for Vision TasksCode2
SKoPe3D: A Synthetic Dataset for Vehicle Keypoint Perception in 3D from Traffic Monitoring Cameras0
Improving the matching of deformable objects by learning to detect keypointsCode0
A lightweight 3D dense facial landmark estimation model from position map dataCode1
Neural Interactive Keypoint DetectionCode1
ClothesNet: An Information-Rich 3D Garment Model Repository with Simulated Clothes Environment0
DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature MatchingCode2
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