NDLI: Beat the MTurkers: Automatic Image Labeling from Weak 3D Supervision

Content Provider	IEEE Xplore Digital Library
Author	Liang-Chieh Chen Fidler, S. Yuille, A.L. Urtasun, R.
Copyright Year	2014
Abstract	Labeling large-scale datasets with very accurate object segmentations is an elaborate task that requires a high degree of quality control and a budget of tens or hundreds of thousands of dollars. Thus, developing solutions that can automatically perform the labeling given only weak supervision is key to reduce this cost. In this paper, we show how to exploit 3D information to automatically generate very accurate object segmentations given annotated 3D bounding boxes. We formulate the problem as the one of inference in a binary Markov random field which exploits appearance models, stereo and/or noisy point clouds, a repository of 3D CAD models as well as topological constraints. We demonstrate the effectiveness of our approach in the context of autonomous driving, and show that we can segment cars with the accuracy of 86% intersection-over-union, performing as well as highly recommended MTurkers!
Starting Page	3198
Ending Page	3205
File Size	1616262
Page Count	8
File Format	PDF
ISBN	9781479951185
ISSN	10636919
DOI	10.1109/CVPR.2014.409
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Three-dimensional displays Solid modeling Design automation Laser radar Image segmentation Training Computational modeling 3D vision Object segmentation crowd-sourcing
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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