Monocular Model-Based 3D Tracking of Rigid Objects: A Survey
Монокулярное модельное 3D-отслеживание жестких объектов: обзор
2005-08-31
SCID: 54.1/3kdbs8a2
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Camera representationModel-based trackingMonocular 3D trackingNatural feature trackingRobust estimation
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Abstract (AI)
Many applications require tracking of complex 3D objects. These include visual servoing of robotic arms on specific target objects, Augmented Reality systems that require real-time registration of the object to be augmented, and head tracking systems that sophisticated interfaces can use. Computer Vision offers solutions that are cheap, practical and non-invasive. This survey reviews the different techniques and approaches that have been developed by industry and research. First, important mathematical tools are introduced: Camera representation, robust estimation and uncertainty estimation. Then a comprehensive study is given of the numerous approaches developed by the Augmented Reality and Robotics communities, beginning with those that are based on point or planar fiducial marks and moving on to those that avoid the need to engineer the environment by relying on natural features such as edges, texture or interest. Recent advances that avoid manual initialization and failures due to fast motion are also presented. The survery concludes with the different possible choices that should be made when implementing a 3D tracking system and a discussion of the future of vision-based 3D tracking. Because it encompasses many computer vision techniques from low-level vision to 3D geometry and includes a comprehensive study of the massive literature on the subject, this survey should be the handbook of the student, the researcher, or the engineer who wants to implement a 3D tracking system.
Key Findings
1
Comprehensive review of tracking approaches from fiducial mark–based methods to natural-feature methods using edges, texture, or interest points.
2
Covers recent advances that eliminate manual initialization and address failures from fast motion in monocular 3D tracking.
3
Positions the survey as a comprehensive reference spanning low-level vision to 3D geometry for students, researchers, and engineers.
4
Provides implementation guidance by comparing design choices for building vision-based 3D tracking systems and discussing future directions.
5
Survey introduces key mathematical tools for monocular 3D rigid object tracking: camera representation, robust estimation, and uncertainty estimation.
Research Object
Rigid 3D objects tracked by monocular model-based computer vision systems
Research Subject
Techniques and approaches for monocular model-based 3D tracking, including camera representation, robust and uncertainty estimation, fiducial- and natural-feature-based methods, initialization and fast-motion robustness, and implementation choices for real-time registration
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2005-08-31
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