  Semantic segmentation and depth estimation are two important tasks in the
area of image processing. Traditionally, these two tasks are addressed in an
independent manner. However, for those applications where geometric and
semantic information is required, such as robotics or autonomous
navigation,depth or semantic segmentation alone are not sufficient. In this
paper, depth estimation and semantic segmentation are addressed together from a
single input image through a hybrid convolutional network. Different from the
state of the art methods where features are extracted by a sole feature
extraction network for both tasks, the proposed HybridNet improves the features
extraction by separating the relevant features for one task from those which
are relevant for both. Experimental results demonstrate that HybridNet results
are comparable with the state of the art methods, as well as the single task
methods that HybridNet is based on.
