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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

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Showing 261–270 of 339 papers

TitleStatusHype
Self-supervised Learning of Interpretable Keypoints from Unlabelled Videos—0
Learning Local Features with Context Aggregation for Visual Localization—0
Learning Markerless Robot-Depth Camera Calibration and End-Effector Pose Estimation—0
Learning to Refine Human Pose Estimation—0
Long-Lived Accurate Keypoints in Event Streams—0
MamKPD: A Simple Mamba Baseline for Real-Time 2D Keypoint Detection—0
Joint Object Contour Points and Semantics for Instance Segmentation—0
MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis—0
MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching—0
Monocular 3D Object Detection via Geometric Reasoning on Keypoints—0
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