  To date, various 3D scene understanding tasks still lack practical and
generalizable pre-trained models, primarily due to the intricate nature of 3D
scene understanding tasks and their immense variations introduced by camera
views, lighting, occlusions, etc. In this paper, we tackle this challenge by
introducing a spatio-temporal representation learning (STRL) framework, capable
of learning from unlabeled 3D point clouds in a self-supervised fashion.
Inspired by how infants learn from visual data in the wild, we explore the rich
spatio-temporal cues derived from the 3D data. Specifically, STRL takes two
temporally-correlated frames from a 3D point cloud sequence as the input,
transforms it with the spatial data augmentation, and learns the invariant
representation self-supervisedly. To corroborate the efficacy of STRL, we
conduct extensive experiments on three types (synthetic, indoor, and outdoor)
of datasets. Experimental results demonstrate that, compared with supervised
learning methods, the learned self-supervised representation facilitates
various models to attain comparable or even better performances while capable
of generalizing pre-trained models to downstream tasks, including 3D shape
classification, 3D object detection, and 3D semantic segmentation. Moreover,
the spatio-temporal contextual cues embedded in 3D point clouds significantly
improve the learned representations.
