Robust 3D Object Tracking from Monocular Images Using Stable Parts
| العنوان: | Robust 3D Object Tracking from Monocular Images Using Stable Parts |
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| المؤلفون: | Alberto Crivellaro, Vincent Lepetit, Yannick Verdie, Mahdi Rad, Pascal Fua, Kwang Moo Yi |
| المصدر: | IEEE Transactions on Pattern Analysis and Machine Intelligence. 40:1465-1479 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Computer science, business.industry, Applied Mathematics, 020207 software engineering, 02 engineering and technology, 3D pose estimation, Articulated body pose estimation, Object detection, Computational Theory and Mathematics, Artificial Intelligence, Robustness (computer science), Video tracking, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Augmented reality, Computer Vision and Pattern Recognition, Artificial intelligence, business, Pose, Software |
| الوصف: | We present an algorithm for estimating the pose of a rigid object in real-time under challenging conditions. Our method effectively handles poorly textured objects in cluttered, changing environments, even when their appearance is corrupted by large occlusions, and it relies on grayscale images to handle metallic environments on which depth cameras would fail. As a result, our method is suitable for practical Augmented Reality applications including industrial environments. At the core of our approach is a novel representation for the 3D pose of object parts: We predict the 3D pose of each part in the form of the 2D projections of a few control points. The advantages of this representation is three-fold: We can predict the 3D pose of the object even when only one part is visible; when several parts are visible, we can easily combine them to compute a better pose of the object; the 3D pose we obtain is usually very accurate, even when only few parts are visible. We show how to use this representation in a robust 3D tracking framework. In addition to extensive comparisons with the state-of-the-art, we demonstrate our method on a practical Augmented Reality application for maintenance assistance in the ATLAS particle detector at CERN. |
| تدمد: | 2160-9292 0162-8828 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08bd3fe421b56ba3da6bb7c2f98a1e26 https://doi.org/10.1109/tpami.2017.2708711 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi.dedup.....08bd3fe421b56ba3da6bb7c2f98a1e26 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21609292 01628828 |
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