基于可变形自注意力和密度不变关键点检测的异源建筑物点云配准方法

doi:10.11947/j.AGCS.2026.20260177

测绘学报 ›› 2026, Vol. 55 ›› Issue (9): 1630-1640.doi: 10.11947/j.AGCS.2026.20260177

基于可变形自注意力和密度不变关键点检测的异源建筑物点云配准方法

刘思涵¹(), 邵世维²^,³, 逯跃锋¹^,⁴(), 丁子淇¹, 牟春霖⁵, 豆孝磊⁵, 吕成哲¹, 贾志蕊¹, 焦振洁¹

^1.山东理工大学建筑工程与空间信息学院，山东　淄博　255049
^2.武汉软件工程职业学院（武汉开放大学），湖北　武汉　430000
^3.湖北复杂零件智能检测与识别工程研究中心，湖北　武汉　430000
^4.国家盐碱地综合利用技术创新中心，山东　东营　257347
^5.中国铁路设计集团有限公司，天津　300308

收稿日期:2026-05-11 修回日期:2026-08-28 出版日期:2026-10-28 发布日期:2026-10-28
通讯作者: 逯跃锋 E-mail:25407010889@stumail.sdut.edu.cn;yflu@sdut.edu.cn
作者简介:刘思涵（2004—），女，硕士生，研究方向为三维数据处理与建模。　E-mail：25407010889@stumail.sdut.edu.cn
基金资助:
国家自然科学基金(42401515);高速铁路轨道系统精密智能检测技术研究(京沪科研-2024-18);高分辨率对地观测系统重大专项(GFZX0404130304);淄博市社会科学规划研究项目(25ZBSK098)

Heterogeneous building point cloud registration via deformable self-attention and density-invariant keypoint detection

Sihan Liu¹(), Shiwei Shao²^,³, Yuefeng Lu¹^,⁴(), Ziqi Ding¹, Chunlin Mu⁵, Xiaolei Dou⁵, Chengzhe Lü¹, Zhirui Jia¹, Zhenjie Jiao¹

^1.School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China
^2.Wuhan Vocational College of Software and Engineering (Wuhan Open University), Wuhan 430000, China
^3.Hubei Engineering Research Center for Intelligent Detection and Identification of Complex Parts, Wuhan 430000, China
^4.National Center of Technology Innovation for Comprehensive Utilization of Saline-Alkali Land, Dongying 257347, China
^5.China Railway Design Corporation, Tianjin 300308, China

Received:2026-05-11 Revised:2026-08-28 Online:2026-10-28 Published:2026-10-28
Contact: Yuefeng Lu E-mail:25407010889@stumail.sdut.edu.cn;yflu@sdut.edu.cn
About author:Liu Sihan (2004—), female, postgraduate, majors in 3D data processing and modeling. E-mail: 25407010889@stumail.sdut.edu.cn
Supported by:
The National Natural Science Foundation of China(42401515);Research on Precision Intelligent Inspection Technology for High-Speed Railway Track Systems(京沪科研-2024-18);The Major Project of High Resolution Earth Observation System of China(GFZX0404130304);Zibo City Social Science Planning Research Project of China(25ZBSK098)

摘要/Abstract

摘要：

针对地面激光扫描与无人机倾斜摄影等异源建筑物点云在采集方式、点云密度、观测视角及空间覆盖范围等方面存在明显差异，导致配准精度不足与稳健性差的问题，本文提出了一种基于可变形自注意力和密度不变关键点检测的异源点云配准方法。该方法在特征提取阶段引入空间可变形自注意力机制，通过动态调整感受野实现全局上下文信息与局部几何特征的协同建模，提升特征表达的判别性；在关键点检测阶段设计密度不变显著性评分策略，有效抑制点云密度变化对关键点筛选的影响，实现跨密度点云的稳定关键点提取；在匹配阶段引入边缘松弛机制优化关键点相似度矩阵，抑制非重叠区域引发的误匹配干扰，并结合RANSAC求解刚性变换参数。试验结果表明，本文方法在3DMatch数据集上特征匹配召回率与配准召回率分别达98.3%和91.6%，在低重叠的3DLoMatch数据集上配准召回率达75.9%，较GeoTransformer提升了2.9个百分点，较Predator提升了17.8个百分点；在自建异源建筑扫描数据集上，本文方法在密度差异显著、低重叠场景中表现稳健，验证了其在实际建筑物三维重建任务中的有效性与适用性。

关键词: 异源点云, 点云配准, 可变形自注意力, 密度不变, 建筑物点云

Abstract:

To address the problems of insufficient registration accuracy and poor robustness caused by significant differences in acquisition modality, point cloud density, viewing perspective, and spatial coverage between terrestrial laser scanning and unmanned aerial vehicle (UAV) oblique photogrammetry point clouds, a heterogeneous point cloud registration method based on deformable self-attention and density-invariant keypoint detection is proposed. In the feature extraction stage, a spatial deformable self-attention mechanism is introduced to dynamically adjust the receptive field, enabling collaborative modeling of global contextual information and local geometric features, thereby enhancing the discriminative capability of feature representations. In the keypoint detection stage, a density-invariant saliency scoring strategy is designed to effectively suppress the influence of point cloud density variations on keypoint selection, achieving stable keypoint extraction across point clouds with different densities. In the matching stage, an edge-relaxation mechanism is incorporated to optimize the keypoint similarity matrix, reducing mismatches caused by non-overlapping regions, while the rigid transformation is estimated using the RANSAC algorithm. Experimental results demonstrate that the proposed method achieves a feature matching recall of 98.3% and a registration recall of 91.6% on the 3DMatch dataset. On the low-overlap 3DLoMatch dataset, it attains a registration recall of 75.9%, outperforming GeoTransformer by 2.9 percentage points and Predator by 17.8 percentage points. Furthermore, experiments conducted on a self-constructed heterogeneous building scanning dataset demonstrate that the proposed method exhibits robust performance under significant density variations and low-overlap conditions, validating its effectiveness and applicability for practical three-dimensional building reconstruction.

Key words: heterogeneous point clouds, point cloud registration, deformable self-attention, density invariance, building point clouds

中图分类号:

P237

刘思涵, 邵世维, 逯跃锋, 丁子淇, 牟春霖, 豆孝磊, 吕成哲, 贾志蕊, 焦振洁. 基于可变形自注意力和密度不变关键点检测的异源建筑物点云配准方法[J]. 测绘学报, 2026, 55(9): 1630-1640.

Sihan Liu, Shiwei Shao, Yuefeng Lu, Ziqi Ding, Chunlin Mu, Xiaolei Dou, Chengzhe Lü, Zhirui Jia, Zhenjie Jiao. Heterogeneous building point cloud registration via deformable self-attention and density-invariant keypoint detection[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(9): 1630-1640.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2026.20260177

http://xb.chinasmp.com/CN/Y2026/V55/I9/1630

图/表 8

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表1

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表2

异源建筑物点云配准误差"

建筑物	方法	各向同性误差		各向异性误差
建筑物	方法	R	t/m	R（x，y，z）	t（x，y，z）/m
1	ICP	178.87°	5.433 6	10.40°，1.51°，176.76°	4.938 7，2.241 8，0.327 0
	D3Feat	0.81°	0.032 1	0.11°，0.69°，0.44°	0.006 3，0.030 8，0.006 6
	Predator	0.46°	0.035 8	0.31°，0.23°，0.32°	0.000 9，0.035 6，0.003 8
	本文方法	0.18°	0.015 1	0.10°，0.13°，0.09°	0.006 4，0.011 9，0.006 7
2	ICP	178.27°	4.160 0	174.85°，36.21°，32.11°	2.679 7，3.110 4，0.671 5
	D3Feat	11.65°	0.567 3	11.62°，1.71°，1.15°	0.032 5，0.039 7，0.565 0
	Predator	0.46°	0.010 3	0.30°，0.35°，0.08°	0.001 3，0.000 6，0.010 2
	本文方法	1.15°	0.051 1	0.33°，0.55°，1.00°	0.041 5，0.005 9，0.029 3
3	ICP	179.99°	2.224 1	0.30°，0.34°，179.86°	1.889 3，1.170 2，0.089 6
	D3Feat	1.44°	0.121 2	1.34°，0.51°，0.03°	0.067 2，0.100 3，0.010 3
	Predator	0.76°	0.063 3	0.00°，0.30°，0.69°	0.006 0，0.062 6，0.007 2
	本文方法	0.29°	0.036 7	0.13°，0.27°，0.02°	0.028 8，0.016 3，0.015 8
4	ICP	175.70°	5.576 7	0.61°，2.68°，175.62°	4.420 2，3.394 9，0.188 5
	D3Feat	13.09°	0.876 5	0.42°，12.52°，3.88°	0.464 3，0.077 7，0.739 3
	Predator	1.85°	0.126 3	1.30°，1.31°，0.34°	0.017 5，0.059 5，0.110 0
	本文方法	1.23°	0.068 4	0.93°，0.54°，0.70°	0.000 1，0.041 0，0.054 8
5	ICP	89.23°	5.160 1	0.12°，1.67°，89.15°	1.083 2，5.042 3，0.169 3
	D3Feat	25.90°	5.198 9	21.93°，7.67°，28.88°	5.143 8，0.250 8，0.711 6
	Predator	0.94°	0.105 6	0.36°，0.86°，0.11°	0.002 5，0.105 5，0.003 6
	本文方法	0.61°	0.042 4	0.26°，0.54°，0.04°	0.001 2，0.042 1，0.004 5
6	ICP	179.19°	4.915 3	22.43°，61.71°，174.72°	4.218 8，2.483 5，0.441 0
	D3Feat	1.19°	0.013 4	1.02°，0.65°，0.68°	0.011 6，0.004 6，0.004 9
	Predator	0.99°	0.050 4	0.66°，0.74°，0.42°	0.048 1，0.005 5，0.013 9
	本文方法	0.52°	0.017 0	0.20°，0.03°，0.41°	0.010 7，0.010 7，0.007 9
7	ICP	124.40°	3.759 6	121.09°，32.56°，22.07°	0.957 1，1.492 6，3.315 3
	D3Feat	1.88°	0.093 4	1.09°，1.26°，0.64°	0.002 1，0.029 6，0.088 5
	Predator	0.96°	0.036 8	0.84°，0.51°，0.14°	0.002 8，0.003 8，0.036 5
	本文方法	1.57°	0.033 5	1.04°，1.10°，0.76°	0.010 8，0.003 4，0.031 5

表2

表3

参考文献 33

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方法	FMR		RR		IR
方法	3DMatch	3DLoMatch	3DMatch	3DLoMatch	3DMatch	3DLoMatch
PerfectMatch	82.9	34.2	50.8	11.0	16.4	4.8
D3Feat	93.1	66.5	77.9	39.1	41.8	15.0
CoFiNet	98.3	82.6	87.0	61.0	52.2	26.9
Predator	96.5	75.3	86.6	58.1	49.3	25.8
GeoTransformer	97.8	88.7	91.1	73.0	85.7	58.3
本文方法	98.3	89.1	91.6	75.9	77.9	49.7

方法	3DLoMatch RR/（%）	自建异源建筑数据集
方法	3DLoMatch RR/（%）	R/（°）	t/m
DSA-DI w/o可变形自注意力模块	71.6	0.92	0.044
DSA-DI w/o密度不变模块	69.2	1.15	0.058
DSA-DI w/o边缘松弛模块	72.3	0.87	0.043
DSA-DI（完整模型）	75.9	0.75	0.036

基于可变形自注意力和密度不变关键点检测的异源建筑物点云配准方法

Heterogeneous building point cloud registration via deformable self-attention and density-invariant keypoint detection

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