Recent works in autonomous driving have widely adopted the bird's-eye-view
(BEV) semantic map as an intermediate representation of the world. Online
prediction of these BEV maps involves non-trivial operations such as
multi-camera data extraction as well as fusion and projection into a common
top-view grid. This is usually done with error-prone geometric operations
(e.g., homography or back-projection from monocular depth estimation) or
expensive direct dense mapping between image pixels and pixels in BEV (e.g.,
with MLP or attention). In this work, we present 'LaRa', an efficient
encoder-decoder, transformer-based model for vehicle semantic segmentation from
multiple cameras. Our approach uses a system of cross-attention to aggregate
information over multiple sensors into a compact, yet rich, collection of
latent representations. These latent representations, after being processed by
a series of self-attention blocks, are then reprojected with a second
cross-attention in the BEV space. We demonstrate that our model outperforms on
nuScenes the best previous works using transformers.