Efficient and accurate segmentation of indoor building structure (IBS) from complex indoor scenes is the primary task of indoor 3D modeling. At present, the research on semantic segmentation of indoor scenes mainly focuses on multiple types of objects, while there are few studies on semantic segmentation of IBS. In this paper, we introduce an efficient and lightweight multiscale hierarchical network MHIBS-Net for semantic segmentation of IBS point clouds. On the one hand, normal vector information (NVI) is added and a relative position encoding (RPE) unit is designed in feature encoding (FE) to better capture the local geometric structure information of the IBS point clouds. On the other hand, the traditional feature decoding (FD) process is improved by a newly designed multilevel hierarchy FD method. In this way, the MHIBS-Net can capture the local and global features of IBS point clouds comprehensively so as to realize automatic and efficient semantic segmentation of IBS point clouds. Finally, several groups of experiments are designed to compare and analyze the proposed MHIBS-Net with some classical semantic segmentation networks, and the Stanford large-scale 3D indoor spaces (S3DIS) dataset is used for experimental verification. Experimental results show that MHIBS-Net can achieve high-precision and high-efficiency automatic semantic segmentation of IBS point clouds in complex indoor environments.

从复杂的室内场景中高效准确地分割室内建筑结构 (IBS) 是室内 3D 建模的主要任务。目前，室内场景语义分割的研究主要集中在多种类型的物体上，而对IBS语义分割的研究较少。在本文中，我们介绍了一种高效轻量级的多尺度分层网络 MHIBS-Net，用于 IBS 点云的语义分割。一方面，在特征编码（FE）中加入法向量信息（NVI）并设计相对位置编码（RPE）单元，以更好地捕捉IBS点云的局部几何结构信息。另一方面，新设计的多级层次FD方法改进了传统的特征解码（FD）过程。这样，MHIBS-Net可以综合捕捉IBS点云的局部和全局特征，实现IBS点云的自动高效语义分割。最后，设计了几组实验，将提出的 MHIBS-Net 与一些经典的语义分割网络进行比较和分析，并使用斯坦福大型 3D 室内空间 (S3DIS) 数据集进行实验验证。实验结果表明，MHIBS-Net可以实现复杂室内环境下IBS点云的高精度、高效率自动语义分割。设计了几组实验来比较和分析所提出的 MHIBS-Net 与一些经典的语义分割网络，并使用斯坦福大型 3D 室内空间 (S3DIS) 数据集进行实验验证。实验结果表明，MHIBS-Net可以实现复杂室内环境下IBS点云的高精度、高效率自动语义分割。设计了几组实验来比较和分析所提出的 MHIBS-Net 与一些经典的语义分割网络，并使用斯坦福大型 3D 室内空间 (S3DIS) 数据集进行实验验证。实验结果表明，MHIBS-Net可以实现复杂室内环境下IBS点云的高精度、高效率自动语义分割。