  The field of self-supervised 3D representation learning has emerged as a
promising solution to alleviate the challenge presented by the scarcity of
extensive, well-annotated datasets. However, it continues to be hindered by the
lack of diverse, large-scale, real-world 3D scene datasets for source data. To
address this shortfall, we propose Generalizable Representation Learning (GRL),
where we devise a generative Bayesian network to produce diverse synthetic
scenes with real-world patterns, and conduct pre-training with a joint
objective. By jointly learning a coarse-to-fine contrastive learning task and
an occlusion-aware reconstruction task, the model is primed with transferable,
geometry-informed representations. Post pre-training on synthetic data, the
acquired knowledge of the model can be seamlessly transferred to two principal
downstream tasks associated with 3D scene understanding, namely 3D object
detection and 3D semantic segmentation, using real-world benchmark datasets. A
thorough series of experiments robustly display our method's consistent
superiority over existing state-of-the-art pre-training approaches.
