  Most existing instance segmentation methods only focus on improving
performance and are not suitable for real-time scenes such as autonomous
driving. This paper proposes a real-time framework that segmenting and
detecting 3D objects by depth. The framework is composed of two parallel
branches: one for instance segmentation and another for object detection. We
discretize the objects' depth into depth categories and transform the instance
segmentation task into a pixel-level classification task. The Mask branch
predicts pixel-level depth categories, and the 3D branch indicates
instance-level depth categories. We produce an instance mask by assigning
pixels which have the same depth categories to each instance. In addition, to
solve the imbalance between mask labels and 3D labels in the KITTI dataset, we
introduce a coarse mask generated by the auto-annotation model to increase
samples. Experiments on the challenging KITTI dataset show that our approach
outperforms LklNet about 1.8 times on the speed of segmentation and 3D
detection.
