Semantic segmentation of 3D mobile laser scanning point clouds is the foundational task for scene understanding in several fields. Most existing segmentation methods tend to simply stack the common point attributes, such as the coordinates and intensity, but ignore their heterogeneous. This paper presents IDA-Net, an intensity-distribution aware network that mines the uniqueness and discrepancy of these two modalities in a separate way for point cloud segmentation under indoor corridor environments. Specifically, IDA-Net consists of two key components. Firstly, an intensity-distribution aware (IDA) descriptor is proposed to mine the intensity distribution pattern. It outputs a multi-channel mask for each point to represent the intensity distribution information. Secondly, a two-stage embedding network is designed to fuse the coordinates and intensity information efficiently. It includes a guiding operation in training stage and a refining operation in testing stage. IDA-Net was evaluated on two indoor corridor areas. Experimental results show that the proposed method significantly improves the performance of segmentation. Specifically, with backbone of KPConv, IDA-Net achieves high mIoU of 90.58% and 88.94% on the above two testing areas respectively, which demonstrates the superiority of the designed IDA descriptor and two-stage embedding network.

3D 移动激光扫描点云的语义分割是多个领域场景理解的基础任务。大多数现有的分割方法倾向于简单地堆叠公共点属性，例如坐标和强度，而忽略它们的异构。本文介绍了 IDA-Net，这是一种强度分布感知网络，它以单独的方式挖掘这两种模式的唯一性和差异性，用于室内走廊环境下的点云分割。具体来说，IDA-Net 由两个关键组件组成。首先，提出了一种强度分布感知（IDA）描述符来挖掘强度分布模式。它为每个点输出一个多通道掩码来表示强度分布信息。第二，设计了一个两阶段的嵌入网络来有效地融合坐标和强度信息。包括训练阶段的引导操作和测试阶段的提炼操作。IDA-Net 在两个室内走廊区域进行了评估。实验结果表明，该方法显着提高了分割的性能。具体来说，通过 KPConv 的主干，IDA-Net 在上述两个测试区域上分别实现了 90.58% 和 88.94% 的高 mIoU，这证明了所设计的 IDA 描述符和两阶段嵌入网络的优越性。实验结果表明，该方法显着提高了分割的性能。具体来说，通过 KPConv 的主干，IDA-Net 在上述两个测试区域上分别实现了 90.58% 和 88.94% 的高 mIoU，这证明了所设计的 IDA 描述符和两阶段嵌入网络的优越性。实验结果表明，该方法显着提高了分割的性能。具体来说，通过 KPConv 的主干，IDA-Net 在上述两个测试区域上分别实现了 90.58% 和 88.94% 的高 mIoU，这证明了所设计的 IDA 描述符和两阶段嵌入网络的优越性。