  With the advent of deep learning, estimating depth from a single RGB image
has recently received a lot of attention, being capable of empowering many
different applications ranging from path planning for robotics to computational
cinematography. Nevertheless, while the depth maps are in their entirety fairly
reliable, the estimates around object discontinuities are still far from
satisfactory. This can be contributed to the fact that the convolutional
operator naturally aggregates features across object discontinuities, resulting
in smooth transitions rather than clear boundaries. Therefore, in order to
circumvent this issue, we propose a novel convolutional operator which is
explicitly tailored to avoid feature aggregation of different object parts. In
particular, our method is based on estimating per-part depth values by means of
superpixels. The proposed convolutional operator, which we dub "Instance
Convolution", then only considers each object part individually on the basis of
the estimated superpixels. Our evaluation with respect to the NYUv2 as well as
the iBims dataset clearly demonstrates the superiority of Instance Convolutions
over the classical convolution at estimating depth around occlusion boundaries,
while producing comparable results elsewhere. Code will be made publicly
available upon acceptance.
