  We present a new approach to instill 4D dynamic object priors into learned 3D
representations by unsupervised pre-training. We observe that dynamic movement
of an object through an environment provides important cues about its
objectness, and thus propose to imbue learned 3D representations with such
dynamic understanding, that can then be effectively transferred to improved
performance in downstream 3D semantic scene understanding tasks. We propose a
new data augmentation scheme leveraging synthetic 3D shapes moving in static 3D
environments, and employ contrastive learning under 3D-4D constraints that
encode 4D invariances into the learned 3D representations. Experiments
demonstrate that our unsupervised representation learning results in
improvement in downstream 3D semantic segmentation, object detection, and
instance segmentation tasks, and moreover, notably improves performance in
data-scarce scenarios.
