  We introduce MultiDepth, a novel training strategy and convolutional neural
network (CNN) architecture that allows approaching single-image depth
estimation (SIDE) as a multi-task problem. SIDE is an important part of road
scene understanding. It, thus, plays a vital role in advanced driver assistance
systems and autonomous vehicles. Best results for the SIDE task so far have
been achieved using deep CNNs. However, optimization of regression problems,
such as estimating depth, is still a challenging task. For the related tasks of
image classification and semantic segmentation, numerous CNN-based methods with
robust training behavior have been proposed. Hence, in order to overcome the
notorious instability and slow convergence of depth value regression during
training, MultiDepth makes use of depth interval classification as an auxiliary
task. The auxiliary task can be disabled at test-time to predict continuous
depth values using the main regression branch more efficiently. We applied
MultiDepth to road scenes and present results on the KITTI depth prediction
dataset. In experiments, we were able to show that end-to-end multi-task
learning with both, regression and classification, is able to considerably
improve training and yield more accurate results.
