  Object viewpoint estimation from 2D images is an essential task in computer
vision. However, two issues hinder its progress: scarcity of training data with
viewpoint annotations, and a lack of powerful features. Inspired by the growing
availability of 3D models, we propose a framework to address both issues by
combining render-based image synthesis and CNNs. We believe that 3D models have
the potential in generating a large number of images of high variation, which
can be well exploited by deep CNN with a high learning capacity. Towards this
goal, we propose a scalable and overfit-resistant image synthesis pipeline,
together with a novel CNN specifically tailored for the viewpoint estimation
task. Experimentally, we show that the viewpoint estimation from our pipeline
can significantly outperform state-of-the-art methods on PASCAL 3D+ benchmark.
