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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 241250 of 339 papers

TitleStatusHype
SHIC: Shape-Image Correspondences with no Keypoint Supervision0
SiLK: Simple Learned Keypoints0
Silver medal Solution for Image Matching Challenge 20240
Sim2Real Object-Centric Keypoint Detection and Description0
SKD: Keypoint Detection for Point Clouds using Saliency Estimation0
SkelFormer: Markerless 3D Pose and Shape Estimation using Skeletal Transformers0
SKoPe3D: A Synthetic Dataset for Vehicle Keypoint Perception in 3D from Traffic Monitoring Cameras0
SKT: Integrating State-Aware Keypoint Trajectories with Vision-Language Models for Robotic Garment Manipulation0
Stereophotoclinometry Revisited0
SuperEvent: Cross-Modal Learning of Event-based Keypoint Detection0
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