  The Bird's-eye View (BeV) representation is widely used for 3D perception
from multi-view camera images. It allows to merge features from different
cameras into a common space, providing a unified representation of the 3D
scene. The key component is the view transformer, which transforms image views
into the BeV. However, actual view transformer methods based on geometry or
cross-attention do not provide a sufficiently detailed representation of the
scene, as they use a sub-sampling of the 3D space that is non-optimal for
modeling the fine structures of the environment. In this paper, we propose
GaussianBeV, a novel method for transforming image features to BeV by finely
representing the scene using a set of 3D gaussians located and oriented in 3D
space. This representation is then splattered to produce the BeV feature map by
adapting recent advances in 3D representation rendering based on gaussian
splatting. GaussianBeV is the first approach to use this 3D gaussian modeling
and 3D scene rendering process online, i.e. without optimizing it on a specific
scene and directly integrated into a single stage model for BeV scene
understanding. Experiments show that the proposed representation is highly
effective and place GaussianBeV as the new state-of-the-art on the BeV semantic
segmentation task on the nuScenes dataset.
