Building a semantic part-based object class detector from synthetic 3D models

This paper presents a new approach for multi-view object class detection based on part models. While most existing approaches have in common that they use real images for training, our approach requires only a database of synthetic 3D models to represent both the appearance and the geometry of an object class. We use semantically equivalent object points on 3D models to build part models and encode the local appearance of the parts by a discriminative learning method that applies AdaBoost to histograms of gradients. The geometric configuration of the parts is represented by spatial distributions which are also directly derived from the 3D models. For recognizing an object in an image, our model provides object hypotheses which are re-ranked with global appearance models. The 2D localization is evaluated on the PASCAL 2006 data set for cars and bicycles, showing that its performance can compete with state-of-the-art detection results.