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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 4150 of 339 papers

TitleStatusHype
2D3D-MATR: 2D-3D Matching Transformer for Detection-free Registration between Images and Point CloudsCode1
Reverse Knowledge Distillation: Training a Large Model using a Small One for Retinal Image Matching on Limited DataCode1
Nonlinear optical encoding enabled by recurrent linear scatteringCode1
SyMFM6D: Symmetry-aware Multi-directional Fusion for Multi-View 6D Object Pose EstimationCode1
NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point CloudCode1
Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty PropagationCode1
KGNv2: Separating Scale and Pose Prediction for Keypoint-based 6-DoF Grasp Synthesis on RGB-D inputCode1
Explicit Box Detection Unifies End-to-End Multi-Person Pose EstimationCode1
NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same ActionCode1
Dense Interspecies Face EmbeddingCode1
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