Active learning for piecewise planar 3D reconstruction
Kowdle A. Chang Y.-J. Gallagher A. Chen T.
Kowdle A.
Chang Y.-J.
Gallagher A.
Citations
Altmetric:
Alternative Title
Abstract
This paper presents an active-learning algorithm for piecewise planar 3D reconstruction of a scene. While previous interactive algorithms require the user to provide tedious interactions to identify all the planes in the scene, we build on successful ideas from the automatic algorithms and introduce the idea of active learning, thereby improving the reconstructions while considerably reducing the effort. Our algorithm first attempts to obtain a piecewise planar reconstruction of the scene automatically through an energy minimization framework. The proposed active-learning algorithm then uses intuitive cues to quantify the uncertainty of the algorithm and suggest regions, querying the user to provide support for the uncertain regions via simple scribbles. These interactions are used to suitably update the algorithm, leading to better reconstructions. We show through machine experiments and a user study that the proposed approach can intelligently query users for interactions on informative regions, and users can achieve better reconstructions of the scene faster, especially for scenes with texture-less surfaces lacking cues like lines which automatic algorithms rely on.
Keywords
Source Title
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Publisher
Series/Report No.
Collections
Rights
Date
2011
DOI
10.1109/CVPR.2011.5995638
Type
Conference Paper