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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 261270 of 339 papers

TitleStatusHype
Neural Outlier Rejection for Self-Supervised Keypoint LearningCode0
SKD: Keypoint Detection for Point Clouds using Saliency Estimation0
Dynamic Convolution: Attention over Convolution KernelsCode0
Self-Supervised 3D Keypoint Learning for Ego-motion EstimationCode0
R2D2: Reliable and Repeatable Detector and DescriptorCode0
Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose EstimationCode1
Simple and Lightweight Human Pose EstimationCode0
ViewSynth: Learning Local Features from Depth using View SynthesisCode0
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose EstimationCode1
Human Keypoint Detection by Progressive Context RefinementCode0
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