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- W4300657050 abstract "Current CNN-based algorithms for recovering the 3D pose of an object in an image assume knowledge about both the object category and its 2D localization in the image. In this paper, we relax one of these constraints and propose to solve the task of joint object category and 3D pose estimation from an image assuming known 2D localization. We design a new architecture for this task composed of a feature network that is shared between subtasks, an object categorization network built on top of the feature network, and a collection of category dependent pose regression networks. We also introduce suitable loss functions and a training method for the new architecture. Experiments on the challenging PASCAL3D+ dataset show state-of-the-art performance in the joint categorization and pose estimation task. Moreover, our performance on the joint task is comparable to the performance of state-of-the-art methods on the simpler 3D pose estimation with known object category task." @default.
- W4300657050 created "2022-10-03" @default.
- W4300657050 creator A5011256828 @default.
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- W4300657050 date "2017-11-20" @default.
- W4300657050 modified "2023-10-09" @default.
- W4300657050 title "Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images" @default.
- W4300657050 doi "https://doi.org/10.48550/arxiv.1711.07426" @default.
- W4300657050 hasPublicationYear "2017" @default.
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