Abstract
3D point cloud segmentation aims to assign semantic labels to individual points in a scene for fine-grained spatial understanding. Existing methods typically adopt data augmentation to alleviate the burden of large-scale annotation. However, most augmentation strategies only focus on local transformations or semantic recomposition, lacking the consideration of global structural dependencies within scenes. To address this limitation, we propose a graph-guided data augmentation framework with dual-level constraints for realistic 3D scene synthesis. Our method learns object relationship statistics from real-world data to construct guiding graphs for scene generation. Local-level constraints enforce geometric plausibility and semantic consistency between objects, while global-level constraints maintain the topological structure of the scene by aligning the generated layout with the guiding graph. Extensive experiments on indoor and outdoor datasets demonstrate that our framework generates diverse and high-quality augmented scenes, leading to consistent improvements in point cloud segmentation performance across various models.
Abstract (translated)
3D点云分割的目标是为场景中的单个点分配语义标签，以实现精细的三维空间理解。现有的方法通常采用数据增强来减轻大规模标注的工作量。然而，大多数增强策略仅关注局部变换或语义重组，缺乏对场景内全局结构依赖性的考虑。为了克服这一限制，我们提出了一种基于图引导的数据增强框架，该框架具有双层约束机制，用于生成现实的三维场景。我们的方法从真实世界数据中学习对象关系统计信息，并以此构建指导图以进行场景生成。局部级别的约束强制执行几何合理性和对象之间的语义一致性，而全局级别的约束通过使生成的布局与指导图对齐来保持场景的拓扑结构。在室内和室外数据集上进行了广泛的实验，结果表明我们的框架能够生成多样化且高质量的数据增强场景，并能在各种模型中显著提升点云分割性能。
URL
https://arxiv.org/abs/2507.22668
