Car Segmentation and Pose Estimation using 3D Object Models
Computer Vision and Pattern Recognition
2016-06-20 v2
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.
Cite
@article{arxiv.1512.06790,
title = {Car Segmentation and Pose Estimation using 3D Object Models},
author = {Siddharth Mahendran and René Vidal},
journal= {arXiv preprint arXiv:1512.06790},
year = {2016}
}