To complete our panoptic approach coined Seamless Scene Segmentation, we add a third stage to Mask R-CNN. Stemming from the same backbone, the “Semantic Head” predicts a dense semantic segmentation over the whole image, also accounting for the uncountable or amorphous classes (e.g. road and sky). The outputs of the Mask and Semantic heads are finally fused using a simple non-maximum suppression algorithm to generate the final panoptic prediction. All details about the actual network architecture, used losses and underlying math can be found at the project website for our CVPR 2019 paper [1].