  Convolutional neural networks have recently shown excellent results in
general object detection and many other tasks. Albeit very effective, they
involve many user-defined design choices. In this paper we want to better
understand these choices by inspecting two key aspects "what did the network
learn?", and "what can the network learn?". We exploit new annotations
(Pascal3D+), to enable a new empirical analysis of the R-CNN detector. Despite
common belief, our results indicate that existing state-of-the-art convnet
architectures are not invariant to various appearance factors. In fact, all
considered networks have similar weak points which cannot be mitigated by
simply increasing the training data (architectural changes are needed). We show
that overall performance can improve when using image renderings for data
augmentation. We report the best known results on the Pascal3D+ detection and
view-point estimation tasks.
