  Understanding 3D environments semantically is pivotal in autonomous driving
applications where multiple computer vision tasks are involved. Multi-task
models provide different types of outputs for a given scene, yielding a more
holistic representation while keeping the computational cost low. We propose a
multi-task model for panoptic segmentation and depth completion using RGB
images and sparse depth maps. Our model successfully predicts fully dense depth
maps and performs semantic segmentation, instance segmentation, and panoptic
segmentation for every input frame. Extensive experiments were done on the
Virtual KITTI 2 dataset and we demonstrate that our model solves multiple
tasks, without a significant increase in computational cost, while keeping high
accuracy performance. Code is available at
https://github.com/juanb09111/PanDepth.git
