The 3D comprehension ability of indoor environments is critical for robots. While deep learning-based methods have improved performance, they require significant amounts of annotated training data. Nevertheless, the cost of scanning and annotating point cloud data in real scenes is high, leading to data scarcity. Consequently, there is an urgent need to investigate data-efficient methods for point cloud instance segmentation. To tackle this issue, we propose to leverage the geometric and scene context knowledge inherent in synthetic data to reduce the need for annotation on real data. Specifically, we simulate the process of human scanning and collecting point cloud data in real-world scenes and construct three large-scale synthetic point cloud datasets using synthetic scenes. The scale of these three datasets is more than ten times that of currently available real-world data. Experimental results demonstrate that the incorporation of synthetic point cloud data can increase instance segmentation performance by over percentage points. Further, to address the problem of domain shift between synthetic and real data, we propose a target-aware pre-training method. It integrates both real and synthetic data during the pre-training process, allowing the model to learn a feature representation that can effectively generalize to downstream real data. Experiments show that our method achieved stable improvements on all three synthetic datasets. The data and code will be publicly available in the future.

中文翻译：

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通过传输合成扫描的知识实现数据高效的 3D 实例分割

室内环境的3D理解能力对于机器人来说至关重要。虽然基于深度学习的方法提高了性能，但它们需要大量带注释的训练数据。然而，在真实场景中扫描和标注点云数据的成本较高，导致数据稀缺。因此，迫切需要研究数据有效的点云实例分割方法。为了解决这个问题，我们建议利用合成数据中固有的几何和场景上下文知识来减少对真实数据进行注释的需要。具体来说，我们模拟人类在现实场景中扫描和收集点云数据的过程，并使用合成场景构建三个大规模合成点云数据集。这三个数据集的规模是当前可用的现实世界数据的十倍以上。实验结果表明，合成点云数据的结合可以将实例分割性能提高多个百分点。此外，为了解决合成数据和真实数据之间的域转移问题，我们提出了一种目标感知预训练方法。它在预训练过程中集成了真实数据和合成数据，使模型能够学习可以有效泛化到下游真实数据的特征表示。实验表明，我们的方法在所有三个合成数据集上都取得了稳定的改进。数据和代码将在未来公开。