Active Learning for Piecewise Planar 3D Reconstruction

In this work, we present 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 the users for interaction...

• Type: Conference
• Year: 2011
• Where: CVPR
• Authors: Adarsh Kowdle, Yao-Jen Chang, Andrew Gallagher, Tsuhan Chen
• Keywords: 3D Reconstruction, Active Learning, Computer Vision, Interactive 3D Reconstruction