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Enhanced three‐dimensional instance segmentation using multi‐feature extracting point cloud neural network
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2025-01-24 , DOI: 10.1111/mice.13430
Hongxu Wang, Jiepeng Liu, Dongsheng Li, Tianze Chen, Pengkun Liu, Han Yan, Yadong Wu
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2025-01-24 , DOI: 10.1111/mice.13430
Hongxu Wang, Jiepeng Liu, Dongsheng Li, Tianze Chen, Pengkun Liu, Han Yan, Yadong Wu
Precise three‐dimensional (3D) instance segmentation of indoor scenes plays a critical role in civil engineering, including reverse engineering, size detection, and advanced structural analysis. However, existing methods often fall short in accurately segmenting complex indoor environments due to challenges of diverse material textures, irregular object shapes, and inadequate datasets. To address these limitations, this paper introduces StructNet3D, a point cloud neural network specifically designed for instance segmentation in indoor components including ceilings, floors, and walls. StructNet3D employs a novel multi‐scale 3D U‐Net backbone integrated with ArchExtract, which designed to capture both global context and local structural details, enabling precise segmentation of diverse indoor environments. Compared to other methods, StructNet3D achieved an AP50 of 87.7 on the proprietary dataset and 68.6 on the S3DIS dataset, demonstrating its effectiveness in accurately segmenting and classifying major structural components within diverse indoor environments.
中文翻译:
使用多特征提取点云神经网络增强的三维实例分割
室内场景的精确三维 (3D) 实例分割在土木工程中起着至关重要的作用,包括逆向工程、尺寸检测和高级结构分析。然而,由于材料纹理多样、物体形状不规则和数据集不足等挑战,现有方法往往无法准确分割复杂的室内环境。为了解决这些限制,本文介绍了 StructNet3D,这是一种点云神经网络,专门用于室内组件(包括天花板、地板和墙壁)中的实例分割。StructNet3D 采用与 ArchExtract 集成的新型多尺度 3D U-Net 主干,旨在捕获全局环境和局部结构细节,从而实现不同室内环境的精确分割。与其他方法相比,StructNet3D 在专有数据集上实现了 87.7 的 AP50 和 S3DIS 数据集上的 68.6,证明了它在准确分割和分类不同室内环境中主要结构组件的有效性。
更新日期:2025-01-24
中文翻译:
使用多特征提取点云神经网络增强的三维实例分割
室内场景的精确三维 (3D) 实例分割在土木工程中起着至关重要的作用,包括逆向工程、尺寸检测和高级结构分析。然而,由于材料纹理多样、物体形状不规则和数据集不足等挑战,现有方法往往无法准确分割复杂的室内环境。为了解决这些限制,本文介绍了 StructNet3D,这是一种点云神经网络,专门用于室内组件(包括天花板、地板和墙壁)中的实例分割。StructNet3D 采用与 ArchExtract 集成的新型多尺度 3D U-Net 主干,旨在捕获全局环境和局部结构细节,从而实现不同室内环境的精确分割。与其他方法相比,StructNet3D 在专有数据集上实现了 87.7 的 AP50 和 S3DIS 数据集上的 68.6,证明了它在准确分割和分类不同室内环境中主要结构组件的有效性。