/Data-Driven 3D Primitives for Single Image Understanding

Data-Driven 3D Primitives for Single Image Understanding

David Fouhey, Abhinav Gupta and Martial Hebert
Conference Paper, International Conference on Computer Vision, December, 2013

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.


What primitives should we use to infer the rich 3D world behind an image? We argue that these primitives should be both visually discriminative and geometrically informative and we present a technique for discovering such primitives. We demonstrate the utility of our primitives by using them to infer 3D surface normals given a single image. Our technique substantially outperforms the state-of-the-art and shows improved cross-dataset performance.

BibTeX Reference
author = {David Fouhey and Abhinav Gupta and Martial Hebert},
title = {Data-Driven 3D Primitives for Single Image Understanding},
booktitle = {International Conference on Computer Vision},
year = {2013},
month = {December},