The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

In this paper, we propose a data-driven approach to leverage repositories of 3D models for scene understanding. Our ability to relate what we see in an image to a large collection of 3D models allows us to transfer information from these models, creating a rich understanding of the scene. We develop a framework for auto-calibrating a camera, rendering 3D models from the viewpoint an image was taken, and computing a similarity measure between each 3D model and an input image. We demonstrate this data-driven approach in the context of geometry estimation and show the ability to find the identities and poses of object in a scene. Additionally, we present a new dataset with annotated scene geometry. This data allows us to measure the performance of our algorithm in 3D, rather than in the image plane.

Notes

Number of pages: 11

Text Reference

Scott Satkin, Jason Lin, and Martial Hebert, "Data-Driven Scene Understanding from 3D Models," Proceedings of the 23rd British Machine Vision Conference (BMVC), September, 2012.

BibTeX Reference

@inproceedings{Satkin_2012_7246,
   author = "Scott Satkin and Jason Lin and Martial Hebert",
   title = "Data-Driven Scene Understanding from 3D Models",
   booktitle = "Proceedings of the 23rd British Machine Vision Conference (BMVC)",
   month = "September",
   year = "2012",
}

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