  Bird's-eye view (BEV) maps are an important geometrically structured
representation widely used in robotics, in particular self-driving vehicles and
terrestrial robots. Existing algorithms either require depth information for
the geometric projection, which is not always reliably available, or are
trained end-to-end in a fully supervised way to map visual first-person
observations to BEV representation, and are therefore restricted to the output
modality they have been trained for. In contrast, we propose a new model
capable of performing zero-shot projections of any modality available in a
first person view to the corresponding BEV map. This is achieved by
disentangling the geometric inverse perspective projection from the modality
transformation, eg. RGB to occupancy. The method is general and we showcase
experiments projecting to BEV three different modalities: semantic
segmentation, motion vectors and object bounding boxes detected in first
person. We experimentally show that the model outperforms competing methods, in
particular the widely used baseline resorting to monocular depth estimation.
