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.

我们提出了一种将 4D 动态物体先验知识注入到学习的 3D 表示中的方法，并利用合成的 3D 形状和对比学习在 3D-4D 约束下进行数据增强，在下游的 3D 语义场景理解任务中，能够有效地提高表现。实验证明，我们的无监督表示学习方法能够在下游的 3D 语义分割、物体检测和实例分割等任务中表现出色，并且在数据稀缺的情况下显著提高了性能。