  Large labeled data sets are one of the essential basics of modern deep
learning techniques. Therefore, there is an increasing need for tools that
allow to label large amounts of data as intuitively as possible. In this paper,
we introduce SALT, a tool to semi-automatically annotate RGB-D video sequences
to generate 3D bounding boxes for full six Degrees of Freedom (DoF) object
poses, as well as pixel-level instance segmentation masks for both RGB and
depth. Besides bounding box propagation through various interpolation
techniques, as well as algorithmically guided instance segmentation, our
pipeline also provides built-in pre-processing functionalities to facilitate
the data set creation process. By making full use of SALT, annotation time can
be reduced by a factor of up to 33.95 for bounding box creation and 8.55 for
RGB segmentation without compromising the quality of the automatically
generated ground truth.
