  We propose the first approach to the problem of inferring the depth map of a
human hand based on a single RGB image. We achieve this with a Convolutional
Neural Network (CNN) that employs a stacked hourglass model as its main
building block. Intermediate supervision is used in several outputs of the
proposed architecture in a staged approach. To aid the process of training and
inference, hand segmentation masks are also estimated in such an intermediate
supervision step, and used to guide the subsequent depth estimation process. In
order to train and evaluate the proposed method we compile and make publicly
available HandRGBD, a new dataset of 20,601 views of hands, each consisting of
an RGB image and an aligned depth map. Based on HandRGBD, we explore variants
of the proposed approach in an ablative study and determine the best performing
one. The results of an extensive experimental evaluation demonstrate that hand
depth estimation from a single RGB frame can be achieved with an accuracy of
22mm, which is comparable to the accuracy achieved by contemporary low-cost
depth cameras. Such a 3D reconstruction of hands based on RGB information is
valuable as a final result on its own right, but also as an input to several
other hand analysis and perception algorithms that require depth input.
Essentially, in such a context, the proposed approach bridges the gap between
RGB and RGBD, by making all existing RGBD-based methods applicable to RGB
input.
