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.

通过利用生成贝叶斯网络产生具有真实世界模式的多样化合成场景，并通过联合学习粗到精的对比学习任务和遮挡感知重建任务，我们提出了可泛化的表示学习（GRL）方法，从而为三维场景的理解提供了可传递、几何信息化的表示。在合成数据的预训练之后，我们的模型的所掌握的知识可以通过真实世界基准数据集实现无缝传递到与三维场景理解相关的两个主要下游任务，即三维物体检测和三维语义分割，并通过详尽系列的实验证明了我们方法相对于现有最新预训练方法的持续优势。