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.

我们提出了一种简单而高效的自监督方法，用于汽车激光雷达点云的 3D 主干。我们在相同场景中捕获的激光雷达扫描的特征之间设计了对比损失。在 Bird's Eye View 平面的 2D 单元格级别上定义的对比度提供了 PointContrast 中利用的点级表示和 TARL 中利用的分割级表示之间的很好的平衡：我们保留了 PointContrast 的简单性（单元格表示计算廉价），同时超过了 TARL 在下游语义分割中的性能。