This paper addresses the challenge of indoor space segmentation from 3D point clouds, which is essential for understanding interior layouts, reconstructing 3D structures, and developing indoor navigation maps. While current deep learning-based methods rely on projecting 3D point clouds into 2D for instance extraction, they often fail to capture the local and global 3D features necessary for effectively segmenting complex indoor spaces, such as multi-ring nested structures. These methods also struggle with generalization across different scenes. In response, this paper proposes an efficient indoor space segmentation method that integrates both 2D and 3D geometric constraints. By leveraging the distribution characteristics of point clouds in 2D and the local and global features in 3D, the method achieves reliable extraction of vertical structural information in complex indoor environments. To address under-segmentation in small spaces due to varying scales, the paper introduces an adaptive extraction method for space partition anchors, guided by local features. During instance-level space segmentation, a hierarchical contour tree structure is employed to precisely partition complex indoor spaces, effectively handling circular and composite structures. The proposed approach was tested on 96 RGB-D scans from the Beike dataset and 6 large-scale indoor scenes from the S3DIS dataset, covering a range of complexities, sizes, and structures. The experimental section includes ablation studies and thorough comparisons with existing state-of-the-art spatial partitioning algorithms based on morphology and deep learning. Results demonstrate that the proposed method significantly outperforms existing approaches in terms of accuracy, robustness, and generalization ability, providing a solid foundation for indoor space modeling and robotic navigation. The source code and datasets will be made publicly available via the “EISPGeo” link.

中文翻译：

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回到几何学：通过 2D-3D 几何约束从点云中高效分割室内空间


本文解决了从 3D 点云分割室内空间的挑战，这对于理解室内布局、重建 3D 结构和开发室内导航地图至关重要。虽然当前基于深度学习的方法依赖于将 3D 点云投影到 2D 中以进行提取，但它们通常无法捕获有效分割复杂室内空间（例如多环嵌套结构）所需的局部和全局 3D 特征。这些方法也难以在不同场景中进行泛化。作为回应，本文提出了一种高效的室内空间分割方法，该方法集成了 2D 和 3D 几何约束。该方法利用二维点云的分布特征和三维中的局部和全局特征，实现了复杂室内环境中垂直结构信息的可靠提取。为了解决由于尺度变化而导致的小空间分割不足的问题，本文引入了一种由局部特征引导的空间分区锚点的自适应提取方法。在实例级空间分割过程中，采用分层轮廓树结构对复杂的室内空间进行精确分区，有效处理圆形和复合结构。所提出的方法在来自 Beike 数据集的 96 个 RGB-D 扫描和 S3DIS 数据集的 6 个大规模室内场景上进行了测试，涵盖了一系列复杂性、大小和结构。实验部分包括消融研究以及与基于形态学和深度学习的现有最先进的空间分区算法的全面比较。 结果表明，所提方法在准确性、鲁棒性和泛化能力方面明显优于现有方法，为室内空间建模和机器人导航提供了坚实的基础。源代码和数据集将通过 “EISPGeo” 链接公开提供。