EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

Locating 3D objects from a single RGB image via Perspective-n-Points (PnP) is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest interpreting PnP as a differentiable layer, so that 2D-3D point correspondences can be partly learned by backpropagatin...

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Veröffentlicht in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 2771 - 2780
Hauptverfasser:	Chen, Hansheng, Wang, Pichao, Wang, Fan, Tian, Wei, Xiong, Lu, Li, Hao
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2022
Schlagworte:	3D from single images; Computer vision theory; Deep learning architectures and techniques; Navigation and autonomous driving; Recognition: detection categorization Computer vision Deep learning Pose estimation Probabilistic logic retrieval; Robot vision; Statistical methods Robot vision systems Statistical analysis Three-dimensional displays
ISSN:	1063-6919
Online-Zugang:	Volltext
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