  We motivate and address a human-in-the-loop variant of the monocular
viewpoint estimation task in which the location and class of one semantic
object keypoint is available at test time. In order to leverage the keypoint
information, we devise a Convolutional Neural Network called Click-Here CNN
(CH-CNN) that integrates the keypoint information with activations from the
layers that process the image. It transforms the keypoint information into a 2D
map that can be used to weigh features from certain parts of the image more
heavily. The weighted sum of these spatial features is combined with global
image features to provide relevant information to the prediction layers. To
train our network, we collect a novel dataset of 3D keypoint annotations on
thousands of CAD models, and synthetically render millions of images with 2D
keypoint information. On test instances from PASCAL 3D+, our model achieves a
mean class accuracy of 90.7%, whereas the state-of-the-art baseline only
obtains 85.7% mean class accuracy, justifying our argument for
human-in-the-loop inference.
