土木与环境工程学报（中英文）2025，Vol.47Issue(5)：12-22,11.DOI:10.11835/j.issn.2096-6717.2024.058
基于深度学习的室内多视角点云自动化配准方法
Towards automated multi-view point cloud registration of indoor scenes using deep learning
摘要
Abstract
Dimensional quality inspection is a necessary step before delivering finished residences.However,traditional manual inspection methods are time-consuming and labor-intensive.As automated dimensional quality inspection using terrestrial laser scanners receives more attention,automated multi-view point cloud registration of indoor scenes becomes more important.Due to the fact that posting targets indoors is inefficient and a large number of repetitive structures fill the indoor scenes of finished residence,it is not suitable to rely solely on natural geometric primitives or top views for target-less registration.In this paper,a deep learning-based automated multi-view point cloud registration method for indoor scenes is proposed.Firstly,the PointAF neural network is used to semantically segment the scanned point cloud data.Then instance segmentation is performed to obtain point cloud instances with different structures.Next,pairwise registration is performed to compute the transformation parameters using door instances.False matches are then removed using an evaluation function based on overlapping confidence and conflict constraints.Finally,multi-view registration is achieved using a spanning tree based sequential registration method.In the validation and comparison experiments,a total of 21 stations of scanned point cloud data from two sets of finished residences are utilized to demonstrate the effectiveness and accuracy of the proposed method.关键词
激光扫描/点云配准/室内场景/深度学习/点云分割Key words
laser scanning/point cloud registration/indoor scenes/deep learning/point cloud segmentation分类
建筑与水利引用本文复制引用
刘界鹏,胡骁,李东声,陈天择,范晓亮,瓮雪冬..基于深度学习的室内多视角点云自动化配准方法[J].土木与环境工程学报（中英文）,2025,47(5):12-22,11.基金项目
国家自然科学基金(52130801、52108283)National Natural Science Foundation of China(Nos.52130801,52108283) （52130801、52108283）
