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arXiv · 1801.08110

The challenge of simultaneous object detection and pose estimation: a comparative study

Daniel Oñoro-Rubio·Roberto J. López-Sastre·Carolina Redondo-Cabrera·Pedro Gil-Jiménez
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Abstract

Detecting objects and estimating their pose remains as one of the major challenges of the computer vision research community. There exists a compromise between localizing the objects and estimating their viewpoints. The detector ideally needs to be view-invariant, while the pose estimation process should be able to generalize towards the category-level. This work is an exploration of using deep learning models for solving both problems simultaneously. For doing so, we propose three novel deep learning architectures, which are able to perform a joint detection and pose estimation, where we gradually decouple the two tasks. We also investigate whether the pose estimation problem should be solved as a classification or regression problem, being this still an open question in the computer vision community. We detail a comparative analysis of all our solutions and the methods that currently define the state of the art for this problem. We use PASCAL3D+ and ObjectNet3D datasets to present the thorough experimental evaluation and main results. With the proposed models we achieve the state-of-the-art performance in both datasets.

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Daniel Oñoro-Rubio, Roberto J. López-Sastre, Carolina Redondo-Cabrera, Pedro Gil-Jiménez. 2018-01-24. The challenge of simultaneous object detection and pose estimation: a comparative study. https://doi.org/10.1016/j.imavis.2018.09.013

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