三维点云语义辅助的多楼层室内空间拓扑模型构建方法

doi:10.11947/j.AGCS.2026.20250382

测绘学报 ›› 2026, Vol. 55 ›› Issue (5): 909-926.doi: 10.11947/j.AGCS.2026.20250382

三维点云语义辅助的多楼层室内空间拓扑模型构建方法

王政文¹(), 杨俊涛¹(), 康志忠², 张宇涛¹, 王旭哲¹, 张雪³

^1.山东科技大学测绘与空间信息学院，山东　青岛　266590
^2.中国地质大学（北京）土地科学技术学院，北京　100083
^3.山东科技大学计算机科学与工程学院，山东　青岛　266590

收稿日期:2025-09-15 修回日期:2026-04-20 出版日期:2026-06-23 发布日期:2026-06-23
通讯作者: 杨俊涛 E-mail:879584130@qq.com;jtyang@sdust.edu.cn
作者简介:王政文（2000—），男，硕士生，研究方向为激光雷达遥感。　E-mail：879584130@qq.com
基金资助:
国家自然科学基金(42201486; 42371453)

A semantic-assisted method for constructing multi-floor indoor spatial topology models from 3D point clouds

Zhengwen WANG¹(), Juntao YANG¹(), Zhizhong KANG², Yutao ZHANG¹, Xuzhe WANG¹, Xue ZHANG³

^1.College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
^2.College of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China
^3.College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Received:2025-09-15 Revised:2026-04-20 Online:2026-06-23 Published:2026-06-23
Contact: Juntao YANG E-mail:879584130@qq.com;jtyang@sdust.edu.cn
About author:WANG Zhengwen (2000—), male, postgraduate, majors in LiDAR remote sensing.　E-mail: 879584130@qq.com
Supported by:
The National Natural Science Foundation of China(42201486; 42371453)

摘要/Abstract

摘要：

室内空间拓扑模型是实现精准导航和个性化位置服务的数据基础。然而，复杂室内公共空间结构层次和拓扑特征灵活多样使得现有方法在多楼层空间剖分与拓扑关系表达方面存在局限性。为此，本文提出一种三维点云语义辅助的多楼层室内空间拓扑模型构建方法。首先，利用三维点云语义信息辅助传统的高度直方图模型和分水岭分割模型，实现初始的“楼层-房间”多层次室内子空间剖分；然后，以墙、地板、天花板等语义为约束量化描述相邻子空间之间的局部连通性，通过构建三维马尔可夫随机场（MRF）模型对初步子空间过分割进行优化，实现局部连续、全局一致的多楼层室内空间剖分；最后，利用门和楼梯分别重建同楼层和跨楼层子空间的连通性，并基于IndoorGML标准建立完整的室内空间三维拓扑关系模型。试验结果表明，本文方法在楼层分割中的召回率、平均交并比和精确率的平均值分别为97.87%、96.05%和98.09%，在房间分割任务中的召回率、平均交并比和精确率的平均值分别为89.87%、85.66%和94.81%，拓扑关系表达的归一化图编辑距离（nGED）上达到81.35%，表明本文构建的拓扑图在节点组成及连通关系方面与真值图具有较高的结构相似性。与现有主流方法相比，本文方法在空间剖分的精确率和召回率上平均提升约9.60和9.30个百分点，在拓扑关系表达的nGED上提高2.49个百分点以上，展现出更强的稳定性与可靠性。

关键词: 三维点云, 空间剖分, 室内拓扑模型, 三维MRF模型, IndoorGML

Abstract:

The indoor space topological model serves as the fundamental data basis for precise navigation and personalized location services. However, the hierarchical complexity and flexible topological characteristics of large-scale indoor public spaces pose limitations for existing methods in multi-floor space partitioning and topological relationship representation. To address this, this paper proposes a semantic-assisted method for constructing multi-floor indoor spatial topology models from 3D point clouds. First, semantic information from 3D point clouds is leveraged to assist traditional height histogram models and watershed segmentation, achieving an initial multi-level “floor-room” partitioning of indoor subspaces. Next, the local connectivity between adjacent subspaces is quantitatively described using semantic constraints such as walls, floors, and ceilings. A 3D Markov random field (MRF) model is then employed to optimize the preliminary over-segmented subspaces, resulting in locally continuous and globally consistent multi-floor indoor space partitioning. Finally, doors and staircases are used to reconstruct intra-floor and inter-floor subspace connectivity, respectively, and a complete 3D indoor topological model is established based on the IndoorGML standard. Experimental results demonstrate that the proposed method achieves recall, mean intersection over union (mIoU), and precision of 97.87%, 96.05%, and 98.09% for floor segmentation, and 89.87%, 85.66%, and 94.81% for room segmentation, with a normalized graph edit distance (nGED) of 81.35% for topological relationship representation. Compared with existing state-of-the-art methods, the proposed approach improves the average precision and recall of space partitioning by approximately 9.60% and 9.30%, respectively, and enhances nGED by over 2.49%, demonstrating greater stability and reliability.

Key words: 3D point clouds, spatial subdivision, indoor topological modeling, 3D Markov random model, IndoorGML

中图分类号:

P237

王政文, 杨俊涛, 康志忠, 张宇涛, 王旭哲, 张雪. 三维点云语义辅助的多楼层室内空间拓扑模型构建方法[J]. 测绘学报, 2026, 55(5): 909-926.

Zhengwen WANG, Juntao YANG, Zhizhong KANG, Yutao ZHANG, Xuzhe WANG, Xue ZHANG. A semantic-assisted method for constructing multi-floor indoor spatial topology models from 3D point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(5): 909-926.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2026.20250382

http://xb.chinasmp.com/CN/Y2026/V55/I5/909

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房间	墙线	门数	房间类型
R0	（E₀，E₃，E₈，E₁₀，E₁₆，E₁₄，E₇，E₂）	3	走廊
R₁	（E₁，E₄，E₉，E₃）	1	普通房间
R₂	（E₅，E₇，E₁₂，E₁₁，E₆）	1	普通房间
R₃	（E₁₂，E₁₄，E₁₅，E₁₃）	1	普通房间

场景	点云数量	面积/m²	房间数	窗户数	门数
区域1	4.402 6×10⁷	24.7×48.3	41	31	47
区域2	4.731 5×10⁷	30.9×51.3	40	8	53
区域3	1.866 2×10⁷	29.3×25.7	23	8	22
区域4	4.347 0×10⁷	47.3×26.6	49	31	59
区域5	7.864 9×10⁷	66.4×45.0	68	51	75
区域6	4.135 3×10⁷	23.2×45.4	48	31	50

场景	点云数量	面积/m²	房间数	窗户数	门数
7y3	6.199×10⁶	22.45×14.40	36	37	41
D7n	8.918×10⁶	27.18×35.10	43	36	44
px4	9.321×10⁶	22.91×41.89	49	65	45
q9v	4.897×10⁶	27.46×18.29	22	11	30
e9z	4.553×10⁶	17.54×14.87	30	28	22
zsn	3.764×10⁶	22.41×13.45	20	22	15
pa4	8.800×10⁶	27.84×30.30	47	47	45
Jmb	4.126×10⁶	16.08×12.39	22	28	15
aay	4.003×10⁶	21.87×11.13	18	17	22
QUC	7.692×10⁶	29.09×44.74	37	59	53
5Lp	5.245×10⁶	23.98×19.48	30	12	19
qoi	4.471×10⁶	12.58×25.28	23	24	29
VFu	6.704×10⁶	31.04×35.82	35	17	49

场景	楼层	语义辅助高度直方图结果			MRF优化结果
场景	楼层	召回率	交并比	精确率	召回率	交并比	精确率
e9z	1层	66.52	66.521	00.00	96.08	96.08	100.00
	2层	99.92	92.87	92.93	99.93	98.52	98.59
	3层	99.04	98.96	99.92	99.04	98.96	99.94
Jmb	1层	90.78	90.32	99.45	99.22	98.67	99.45
Jmb	2层	99.31	88.19	88.73	99.38	98.52	99.13
pa4	1层	99.93	98.36	98.43	99.93	98.36	98.43
	2层	97.52	92.42	94.65	97.13	96.15	98.95
	3层	95.75	92.48	96.44	98.99	97.53	98.52
	4层	81.41	80.81	99.10	96.74	95.02	98.17
qoi	1层	99.91	94.51	94.59	99.86	98.57	98.70
qoi	2层	95.50	95.43	99.92	98.88	98.76	99.88
VFu	1层	99.92	98.93	99.01	99.92	98.93	99.01
	2层	99.62	93.46	93.79	99.39	97.79	98.38
	3层	91.01	91.01	100.00	97.51	97.13	99.61
平均值		96.95	94.57	97.51	97.87	96.05	98.09

场景	楼层	语义辅助直方图结果			MRF优化结果
场景	楼层	召回率	交并比	精确率	召回率	交并比	精确率
e9z	1层	81.82	81.82	100.00	90.91	90.91	100.00
	2层	72.73	72.73	100.00	90.91	90.91	100.00
	3层	100.00	100.00	100.00	100.00	100.00	100.00
Jmb	1层	75.00	69.23	90.00	91.67	91.67	100.00
Jmb	2层	70.00	63.64	87.50	90.00	90.00	100.00
pa4	1层	66.67	66.67	100.00	83.33	83.33	100.00
	2层	73.68	63.64	82.35	89.47	80.95	89.47
	3层	77.78	70.00	87.50	88.89	88.89	100.00
	4层	100.00	80.00	80.00	100.00	100.00	100.00
qoi	1层	90.00	90.00	100.00	90.00	90.00	100.00
qoi	2层	92.31	92.31	100.00	84.62	84.62	100.00
VFu	1层	81.82	69.23	81.82	81.82	75.00	90.00
	2层	80.00	75.00	92.31	84.62	84.62	100.00
	3层	88.89	80.00	88.89	88.89	80.00	88.89
平均值		87.85	83.02	93.45	89.87	85.66	94.81

三维点云语义辅助的多楼层室内空间拓扑模型构建方法

A semantic-assisted method for constructing multi-floor indoor spatial topology models from 3D point clouds

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Metrics

本文评价

模型	精确率	召回率	平均交并比
Hydia	86.18	77.55	—
HOV-SG	84.10	83.59	—
本文模型（优化前）	93.78	87.85	83.02
本文模型（优化后）	94.81	89.87	85.66

场景	nGED
场景	BIMNet	本文
e9z	90.10	90.41
7y3	73.33	83.08
px4	80.45	81.25
px4_2	100.00	100.00
d7n	84.78	89.69
zsn	78.02	76.47
q9v	68.33	71.51

[1]	韩李涛, 周丽娟, 龚城, 张爱国. 顾及步行习惯的室内导航网络及其生成算法[J]. 测绘学报, 2022, 51(5): 729-738.
[2]	郑顺义, 何源, 徐刚, 王辰, 朱锋博. 三维点云数据实时管理的Hash map方法[J]. 测绘学报, 2018, 47(6): 825-832.

场景	房间初分割结果			MRF优化结果
场景	召回率	交并比	精确率	召回率	交并比	精确率
区域1	82.93	75.56	89.47	90.26	86.05	94.87
区域2	77.50	72.09	91.18	85.00	82.93	97.14
区域3	78.26	66.67	81.82	86.96	80.00	90.91
区域4	77.55	71.70	90.48	83.67	78.84	93.18
区域5	85.29	81.69	95.08	88.24	84.51	95.24
区域6	83.33	76.92	90.91	89.36	85.71	95.46
平均值	81.41	75.26	90.87	87.31	83.27	94.74