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

Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views