  Bird's-eye View (BeV) representations have emerged as the de-facto shared
space in driving applications, offering a unified space for sensor data fusion
and supporting various downstream tasks. However, conventional models use grids
with fixed resolution and range and face computational inefficiencies due to
the uniform allocation of resources across all cells. To address this, we
propose PointBeV, a novel sparse BeV segmentation model operating on sparse BeV
cells instead of dense grids. This approach offers precise control over memory
usage, enabling the use of long temporal contexts and accommodating
memory-constrained platforms. PointBeV employs an efficient two-pass strategy
for training, enabling focused computation on regions of interest. At inference
time, it can be used with various memory/performance trade-offs and flexibly
adjusts to new specific use cases. PointBeV achieves state-of-the-art results
on the nuScenes dataset for vehicle, pedestrian, and lane segmentation,
showcasing superior performance in static and temporal settings despite being
trained solely with sparse signals. We will release our code along with two new
efficient modules used in the architecture: Sparse Feature Pulling, designed
for the effective extraction of features from images to BeV, and Submanifold
Attention, which enables efficient temporal modeling. Our code is available at
https://github.com/valeoai/PointBeV.
