  The sensing process of large-scale LiDAR point clouds inevitably causes large
blind spots, i.e. regions not visible to the sensor. We demonstrate how these
inherent sampling properties can be effectively utilized for self-supervised
representation learning by designing a highly effective pre-training framework
that considerably reduces the need for tedious 3D annotations to train
state-of-the-art object detectors. Our Masked AutoEncoder for LiDAR point
clouds (MAELi) intuitively leverages the sparsity of LiDAR point clouds in both
the encoder and decoder during reconstruction. This results in more expressive
and useful initialization, which can be directly applied to downstream
perception tasks, such as 3D object detection or semantic segmentation for
autonomous driving. In a novel reconstruction approach, MAELi distinguishes
between empty and occluded space and employs a new masking strategy that
targets the LiDAR's inherent spherical projection. Thereby, without any ground
truth whatsoever and trained on single frames only, MAELi obtains an
understanding of the underlying 3D scene geometry and semantics. To demonstrate
the potential of MAELi, we pre-train backbones in an end-to-end manner and show
the effectiveness of our unsupervised pre-trained weights on the tasks of 3D
object detection and semantic segmentation.
