Pose Estimation Techniques in Computer Vision
Summary
Pose estimation refers to determination of the orientation and position of objects or cameras within a scene. Techniques span from classical geometric methods that exploit point correspondences and epipolar constraints to modern deep learning approaches that infer pose from image data directly. Geometric pipelines typically solve minimal problems such as the perspective-n-point (PnP) formulation or recover affine correspondences under known calibration. Stereo or multi-view systems combine multiple camera viewpoints to constrain depth and improve accuracy. Monocular methods often fuse depth sensors or exploit temporal coherence to mitigate scale ambiguity. In parallel, learning-based frameworks employ convolutional neural networks to detect keypoints and regress rotation and translation parameters, extending to 2D human pose, 3D skeletal tracking and six-degrees-of-freedom (6-DoF) object localisation. Recent research has advanced self-supervised learning, real-time performance and robustness under occlusion, while applications range from augmented reality and robotic manipulation to autonomous navigation.
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Pose Estimation Techniques in Computer Vision publication trend
The graph below shows the total number of articles in pose estimation techniques in computer vision across all publications each year (not limited to Nature Index journals).
Technical terms
Pose estimation: Determination of the position and orientation of an object or camera in space.
6-DoF: Six degrees of freedom; three translational and three rotational axes defining spatial pose.
Perspective-n-Point problem: Geometric formulation to recover camera pose from correspondences between n 3D points and their 2D projections.
Affine correspondence: A point match enriched with local affine transformation parameters such as scale and orientation.
Epipolar geometry: Relationship between two camera views that constrains corresponding points to lie on epipolar lines.
Stereo vision: Use of two or more cameras to infer depth and pose from parallax between views.
Monocular vision: Pose estimation from a single camera, often requiring additional constraints to resolve depth ambiguity.
References
- On Making SIFT Features Affine Covariant. International Journal of Computer Vision (2023).
- Research on pose estimation for stereo vision measurement system by an improved method: uncertainty weighted stereopsis pose solution method based on projection vector.. Optics Express (2020).
- Improve the Estimation of Monocular Vision 6-DOF Pose Based on the Fusion of Camera and Laser Rangefinder. Remote Sensing (2021).
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