Enhanced three-dimensional instance segmentation using multi-feature extracting point cloud neural network

IF 8.5 1区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer-Aided Civil and Infrastructure Engineering Pub Date : 2025-01-23 DOI:10.1111/mice.13430

Hongxu Wang, Jiepeng Liu, Dongsheng Li, Tianze Chen, Pengkun Liu, Han Yan, Yadong Wu

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引用次数: 0

Abstract

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.

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利用多特征提取点云神经网络增强三维实例分割

室内场景的精确三维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，由于材料纹理的多样性、物体形状的不规则性和数据集的不足，现有的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，这是一个专门为室内组件（包括天花板、地板和墙壁）的实例分割而设计的点云神经网络。StructNet3D采用了一种新型的多尺度3D U-Net骨干网，与ArchExtract集成在一起，旨在捕捉全局背景和局部结构细节，从而实现对不同室内环境的精确分割。与其他方法相比，StructNet3D在专有数据集上的AP50为87.7，在S3DIS数据集上的AP50为68.6，证明了其在不同室内环境下准确分割和分类主要结构部件的有效性。

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来源期刊

Computer-Aided Civil and Infrastructure Engineering 工程技术-工程：土木

CiteScore

17.60

自引率

19.80%

发文量

146

审稿时长

1 months

期刊介绍： Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms. Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.