Car Segmentation and Pose Estimation using 3D Object Models

Siddharth Mahendran; René Vidal

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arXiv · 1512.06790

Car Segmentation and Pose Estimation using 3D Object Models

Siddharth Mahendran·René Vidal

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Abstract

Image segmentation and 3D pose estimation are two key cogs in any algorithm for scene understanding. However, state-of-the-art CRF-based models for image segmentation rely mostly on 2D object models to construct top-down high-order potentials. In this paper, we propose new top-down potentials for image segmentation and pose estimation based on the shape and volume of a 3D object model. We show that these complex top-down potentials can be easily decomposed into standard forms for efficient inference in both the segmentation and pose estimation tasks. Experiments on a car dataset show that knowledge of segmentation helps perform pose estimation better and vice versa.

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BibTeX RIS

Siddharth Mahendran, René Vidal. 2016-06-17. Car Segmentation and Pose Estimation using 3D Object Models. https://arxiv.org/abs/1512.06790

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