  Increasing the accuracy of instance segmentation methods is often done at the
expense of speed. Using coarser representations, we can reduce the number of
parameters and thus obtain real-time masks. In this paper, we take inspiration
from the set cover problem to predict mask approximations. Given ground-truth
binary masks of objects of interest as training input, our method learns to
predict the approximate coverage of these objects by disks without supervision
on their location or radius. Each object is represented by a fixed number of
disks with different radii. In the learning phase, we consider the radius as
proportional to a standard deviation in order to compute the error to propagate
on a set of two-dimensional Gaussian functions rather than disks. We trained
and tested our instance segmentation method on challenging datasets showing
dense urban settings with various road users. Our method achieve state-of-the
art results on the IDD and KITTI dataset with an inference time of 0.040 s on a
single RTX 3090 GPU.
