Monocular model-based 3D tracking of rigid objects: a survey
Open accessAbstract
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.
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Edges and corners detectors are retained to be more robust than feature detectors in case of partial occlusion of the target, especially during tracking.
“Furthermore, 360 edges and corners detectors are retained to be more robust than features detectors in case of partial occlusion of the target, especially during tracking [42].”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [42]
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Comellini, A., Casu, D., Zenou, E., Dubanchet, V., & Espinosa, C. (2020) · Journal of Guidance, Control, and Dynamics · doi:10.2514/1.g005034 · Cited by 12 · see citing works
CubeSat-CDT: A cross-domain dataset for 6-DoF trajectory estimation of a symmetric spacecraft
Musallam, M. A., Rathinam, A., Gaudillière, V., Castillo, M. O. D., & Aouada, D. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-25056-9_8 · Cited by 8 · see citing works
- Authors
- Lepetit, V., & Fua, P.
- Affiliations
- Computer Vision Laboratory (CH)
- Venue
- Foundations and Trends® in Computer Graphics and Vision
- Publisher
- Emerald
- Published
- 2005-12-10
- Volume
- 1
- Issue
- 1
- Pages
- 1-89
- ISSN
- 1572-2740 (ISSN-L)
- 1572-2759
- OpenAlex
- W4206771003
- Cited by
- 484
- References
- 111
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 11 · 2023: 18 · 2022: 12 · 2021: 26 · 2020: 32 · 2019: 32 · 2018: 30 · 2017: 26 · 2016: 27 · 2015: 27 · 2014: 24 · 2013: 31 · 2012: 28
- FWCI
- 21.05
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Licence
- cc-by-nc-nd
- Locations
- Foundations and Trends® in Computer Graphics and Vision (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- 3D object tracking, monocular vision, rigid object tracking, augmented reality, visual servoing, robotic arm control, head tracking, fiducial markers, natural feature tracking, edge detection, texture features, interest points, robust estimation, uncertainty estimation, real-time registration
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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