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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
A Graph-Based Approach for Category-Agnostic Pose EstimationCode2
Instance-Adaptive and Geometric-Aware Keypoint Learning for Category-Level 6D Object Pose EstimationCode2
DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature MatchingCode2
X-Pose: Detecting Any KeypointsCode2
DaD: Distilled Reinforcement Learning for Diverse Keypoint DetectionCode2
Detector-Free Structure from MotionCode2
GSplatLoc: Grounding Keypoint Descriptors into 3D Gaussian Splatting for Improved Visual LocalizationCode2
InstructDiffusion: A Generalist Modeling Interface for Vision TasksCode2
Objects as PointsCode2
Center Direction Network for Grasping Point Localization on ClothsCode1
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