  We propose a new self-supervised method for pre-training the backbone of deep
perception models operating on point clouds. The core idea is to train the
model on a pretext task which is the reconstruction of the surface on which the
3D points are sampled, and to use the underlying latent vectors as input to the
perception head. The intuition is that if the network is able to reconstruct
the scene surface, given only sparse input points, then it probably also
captures some fragments of semantic information, that can be used to boost an
actual perception task. This principle has a very simple formulation, which
makes it both easy to implement and widely applicable to a large range of 3D
sensors and deep networks performing semantic segmentation or object detection.
In fact, it supports a single-stream pipeline, as opposed to most contrastive
learning approaches, allowing training on limited resources. We conducted
extensive experiments on various autonomous driving datasets, involving very
different kinds of lidars, for both semantic segmentation and object detection.
The results show the effectiveness of our method to learn useful
representations without any annotation, compared to existing approaches. Code
is available at https://github.com/valeoai/ALSO
