Virtual Training for Multi-View Object Class Recognition

Our goal is to circumvent one of the roadblocks to using existing approaches for single-view recognition for achieving multi-view recognition, namely, the need for sufficient training data for many viewpoints. We show how to construct virtual training examples for multi-view recognition using a simple model of objects (nearly planar facades centered at fixed 3D positions). We also show how the models can be learned from a few labeled images for each class.

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Han-Pang Chiu, Leslie Pack Kaelbling, Tomás Lozano-Pérez
• Keywords: Computer Vision, CVPR 2007, Multi-view Recognition, Sufficient Training Data