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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 3140 of 339 papers

TitleStatusHype
Enhancing Scene Coordinate Regression with Efficient Keypoint Detection and Sequential InformationCode1
3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARsCode1
EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual LocalizationCode1
Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion ApproachCode1
Dense Interspecies Face EmbeddingCode1
Nonlinear optical encoding enabled by recurrent linear scatteringCode1
Deep Dual Consecutive Network for Human Pose EstimationCode1
ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor ExtractionCode1
Bottom-Up Human Pose Estimation Via Disentangled Keypoint RegressionCode1
Deep High-Resolution Representation Learning for Human Pose EstimationCode1
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