Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
Three-dimensional object recognition is crucial in modern applications, including robotics in manufacturing, household items, augmented and virtual reality, and autonomous driving. Extensive research and numerous surveys have been conducted in this field. This study aims to create a model selection...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/6/3284 |
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| author | Kostas Ordoumpozanis George A Papakostas |
| author_facet | Kostas Ordoumpozanis George A Papakostas |
| author_sort | Kostas Ordoumpozanis |
| collection | DOAJ |
| description | Three-dimensional object recognition is crucial in modern applications, including robotics in manufacturing, household items, augmented and virtual reality, and autonomous driving. Extensive research and numerous surveys have been conducted in this field. This study aims to create a model selection guide by addressing key questions we need to answer when we want to select a 6D pose estimation model: inputs, modalities, real-time capabilities, hardware requirements, evaluation datasets, performance metrics, strengths, limitations, and special attributes such as symmetry or occlusion handling. By analyzing 84 models, including 62 new ones beyond previous surveys, and identifying 25 datasets 14 newly introduced, we organized the results into comparison tables and standardized summarization templates. This structured approach facilitates easy model comparison and selection based on practical application needs. The focus of this study is on the practical aspects of utilizing 6D pose estimation models, providing a valuable resource for researchers and practitioners. |
| format | Article |
| id | doaj-art-3bd31a2fea1c4fa993445bf3c7e21d06 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3bd31a2fea1c4fa993445bf3c7e21d062025-08-20T02:42:41ZengMDPI AGApplied Sciences2076-34172025-03-01156328410.3390/app15063284Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application FieldsKostas Ordoumpozanis0George A Papakostas1Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilini, GreeceMLV Research Group, Department of Informatics, Democritus University of Thrace, 65404 Kavala, GreeceThree-dimensional object recognition is crucial in modern applications, including robotics in manufacturing, household items, augmented and virtual reality, and autonomous driving. Extensive research and numerous surveys have been conducted in this field. This study aims to create a model selection guide by addressing key questions we need to answer when we want to select a 6D pose estimation model: inputs, modalities, real-time capabilities, hardware requirements, evaluation datasets, performance metrics, strengths, limitations, and special attributes such as symmetry or occlusion handling. By analyzing 84 models, including 62 new ones beyond previous surveys, and identifying 25 datasets 14 newly introduced, we organized the results into comparison tables and standardized summarization templates. This structured approach facilitates easy model comparison and selection based on practical application needs. The focus of this study is on the practical aspects of utilizing 6D pose estimation models, providing a valuable resource for researchers and practitioners.https://www.mdpi.com/2076-3417/15/6/3284computer vision3D object detection6D pose estimationdeep learningobject localizationaugmented reality |
| spellingShingle | Kostas Ordoumpozanis George A Papakostas Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields Applied Sciences computer vision 3D object detection 6D pose estimation deep learning object localization augmented reality |
| title | Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields |
| title_full | Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields |
| title_fullStr | Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields |
| title_full_unstemmed | Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields |
| title_short | Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields |
| title_sort | reviewing 6d pose estimation model strengths limitations and application fields |
| topic | computer vision 3D object detection 6D pose estimation deep learning object localization augmented reality |
| url | https://www.mdpi.com/2076-3417/15/6/3284 |
| work_keys_str_mv | AT kostasordoumpozanis reviewing6dposeestimationmodelstrengthslimitationsandapplicationfields AT georgeapapakostas reviewing6dposeestimationmodelstrengthslimitationsandapplicationfields |