  Pseudo depth maps are depth map predicitions which are used as ground truth
during training. In this paper we leverage pseudo depth maps in order to
segment objects of classes that have never been seen during training. This
renders our object segmentation task an open world task. The pseudo depth maps
are generated using pretrained networks, which have either been trained with
the full intention to generalize to downstream tasks (LeRes and MiDaS), or
which have been trained in an unsupervised fashion on video sequences
(MonodepthV2). In order to tell our network which object to segment, we provide
the network with a single click on the object's surface on the pseudo depth map
of the image as input. We test our approach on two different scenarios: One
without the RGB image and one where the RGB image is part of the input. Our
results demonstrate a considerably better generalization performance from seen
to unseen object types when depth is used. On the Semantic Boundaries Dataset
we achieve an improvement from $61.57$ to $69.79$ IoU score on unseen classes,
when only using half of the training classes during training and performing the
segmentation on depth maps only.
