  Crowdsourced 3D CAD models are becoming easily accessible online, and can
potentially generate an infinite number of training images for almost any
object category.We show that augmenting the training data of contemporary Deep
Convolutional Neural Net (DCNN) models with such synthetic data can be
effective, especially when real training data is limited or not well matched to
the target domain. Most freely available CAD models capture 3D shape but are
often missing other low level cues, such as realistic object texture, pose, or
background. In a detailed analysis, we use synthetic CAD-rendered images to
probe the ability of DCNN to learn without these cues, with surprising
findings. In particular, we show that when the DCNN is fine-tuned on the target
detection task, it exhibits a large degree of invariance to missing low-level
cues, but, when pretrained on generic ImageNet classification, it learns better
when the low-level cues are simulated. We show that our synthetic DCNN training
approach significantly outperforms previous methods on the PASCAL VOC2007
dataset when learning in the few-shot scenario and improves performance in a
domain shift scenario on the Office benchmark.
