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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 5160 of 339 papers

TitleStatusHype
A lightweight 3D dense facial landmark estimation model from position map dataCode1
Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion ApproachCode1
Edge Weight Prediction For Category-Agnostic Pose EstimationCode1
GPU optimization of the 3D Scale-invariant Feature Transform Algorithm and a Novel BRIEF-inspired 3D Fast DescriptorCode1
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud RegistrationCode1
CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud RegistrationCode1
End-to-End Trainable Multi-Instance Pose Estimation with TransformersCode1
EP2P-Loc: End-to-End 3D Point to 2D Pixel Localization for Large-Scale Visual LocalizationCode1
Fast Fourier ConvolutionCode1
Benchmarking Fish Dataset and Evaluation Metric in Keypoint Detection -- Towards Precise Fish Morphological Assessment in Aquaculture BreedingCode1
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