BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point
  Clouds
Corentin Sautier Gilles Puy Alexandre Boulch Renaud Marlet Vincent Lepetit
We present a surprisingly simple and efficient method for self-supervision of
3D backbone on automotive Lidar point clouds. We design a contrastive loss
between features of Lidar scans captured in the same scene. Several such
approaches have been proposed in the literature from PointConstrast, which uses
a contrast at the level of points, to the state-of-the-art TARL, which uses a
contrast at the level of segments, roughly corresponding to objects. While the
former enjoys a great simplicity of implementation, it is surpassed by the
latter, which however requires a costly pre-processing. In BEVContrast, we
define our contrast at the level of 2D cells in the Bird's Eye View plane.
Resulting cell-level representations offer a good trade-off between the
point-level representations exploited in PointContrast and segment-level
representations exploited in TARL: we retain the simplicity of PointContrast
(cell representations are cheap to compute) while surpassing the performance of
TARL in downstream semantic segmentation.