arXiv:1512.06790cs.CV
Car Segmentation and Pose Estimation using 3D Object Models
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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.
> Even for 2D picture we build mental models of the scene in ways image classifiers flat out don't and can't. That's not true. Conditional Random Fields and other statistical models are being used to model spatial object relations. Example: https://arxiv.org/abs/1512.06790v2
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