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Amodal perception terms the ability of humans to imagine the entire shapes ofoccluded objects. This gives humans an advantage to keep track of everythingthat is going on, especially in crowded situations. Typical perceptionfunctions, however, lack amodal perception abilities and are therefore at adisadvantage in situations with occlusions. Complex urban driving scenariosoften experience many different types of occlusions and, therefore, amodalperception for automated vehicles is an important task to investigate. In thispaper, we consider the task of amodal semantic segmentation and propose ageneric way to generate datasets to train amodal semantic segmentation methods.We use this approach to generate an amodal Cityscapes dataset. Moreover, wepropose and evaluate a method as baseline on Amodal Cityscapes, showing itsapplicability for amodal semantic segmentation in automotive environmentperception. We provide the means to re-generate this dataset on github.

Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge Baseline