  Semantic amodal segmentation is a recently proposed extension to
instance-aware segmentation that includes the prediction of the invisible
region of each object instance. We present the first all-in-one end-to-end
trainable model for semantic amodal segmentation that predicts the amodal
instance masks as well as their visible and invisible part in a single forward
pass. In a detailed analysis, we provide experiments to show which architecture
choices are beneficial for an all-in-one amodal segmentation model. On the COCO
amodal dataset, our model outperforms the current baseline for amodal
segmentation by a large margin. To further evaluate our model, we provide two
new datasets with ground truth for semantic amodal segmentation, D2S amodal and
COCOA cls. For both datasets, our model provides a strong baseline performance.
Using special data augmentation techniques, we show that amodal segmentation on
D2S amodal is possible with reasonable performance, even without providing
amodal training data.
