基于多重特征提取和点对应关系的三维点云非刚配准

doi:10.11996/JG.j.2095-302X.2025010150

图学学报 ›› 2025, Vol. 46 ›› Issue (1): 150-158.DOI: 10.11996/JG.j.2095-302X.2025010150

• 计算机图形学与虚拟现实 • 上一篇下一篇

基于多重特征提取和点对应关系的三维点云非刚配准

吴亦奇¹(), 何嘉乐¹, 张甜甜¹, 张德军¹, 李艳丽¹^,², 陈壹林³()

1.中国地质大学(武汉)计算机学院，湖北武汉 430078
2.鄂尔多斯职业学院信息工程系，内蒙古鄂尔多斯 017000
3.智能机器人湖北省重点实验室(武汉工程大学)，湖北武汉 430205

收稿日期:2024-07-09 接受日期:2024-09-22 出版日期:2025-02-28 发布日期:2025-02-14
通讯作者:陈壹林(1989-)，男，讲师，博士。主要研究方向为人工智能。E-mail：yilinchen@wit.edu.cn
第一作者:吴亦奇(1985-)，男，副教授，博士。主要研究方向为图形图像处理。E-mail：wuyq@cug.edu.cn
基金资助:
湖北省教育厅科研计划项目(Q20221501);内蒙古自治区高等学校科学研究项目(NJZY21164)

Unsupervised 3D point cloud non-rigid registration based on multi-feature extraction and point correspondence

WU Yiqi¹(), HE Jiale¹, ZHANG Tiantian¹, ZHANG Dejun¹, LI Yanli¹^,², CHEN Yilin³()

1. School of Computer Science, China University of Geosciences, Wuhan Hubei 430078, China
2. Department of Information Engineering, Ordos Vocational College, Ordos Inner Mongolia 017000, China
3. Hubei Key Laboratory of Intelligent Robot (Wuhan Institute of Technology), Wuhan Hubei 430205, China

Received:2024-07-09 Accepted:2024-09-22 Published:2025-02-28 Online:2025-02-14
Contact: CHEN Yilin (1989-), lecturer, Ph.D. His main research interest covers artificial intelligence. E-mail：yilinchen@wit.edu.cn
First author：WU Yiqi (1985-), associate professor, Ph.D. His main research interests cover graphic and image processing. E-mail：wuyq@cug.edu.cn
Supported by:
The Science and Technology Research Project of Education Department of Hubei Province(Q20221501);Inner Mongolia Autonomous Region Higher Education Scientific Research Project(NJZY21164)

摘要/Abstract

摘要：

为实现非刚点云间的精确配准，并在配准过程中准确建立点对应关系，提出了一种基于多重特征提取和点对应关系建模的无监督三维点云非刚配准网络。网络由多重特征提取、匹配精细化和形状感知注意力模块构成。首先，提取输入的源点云与目标点云的多重特征，并计算特征之间的相似度获得特征相似度矩阵。随后，将特征相似矩阵输入到网络中的匹配精细化模块中使用软硬匹配结合的方法生成点对应关系矩阵。最后，将目标点云的特征、源点云和点对应关系矩阵输入形状感知注意力模块，得到最终配准结果。通过此方法，配准结果可以同时具有与目标点云的点对应关系和形状相似性。在公共数据集及合成数据集上进行实验，可视化效果及定量结果比较表明，该方法可准确获得源点云与目标点云间的点对应关系和形状相似性，有效实现无监督三维点云非刚配准。

关键词: 点云, 非刚配准, 点对应关系, 形状感知注意力

Abstract:

To achieve accurate registration between non-rigid point clouds while ensuring the precise establishment of point correspondences, an unsupervised 3D point cloud non-rigid registration network based on multi-feature extraction and point correspondence was proposed. The network comprised modules for multi-feature extraction, matching refinement, and shape-aware attention. Firstly, multiple features were extracted from the input source and target point clouds, and the feature similarity matrix was obtained by feature similarity calculation. Subsequently, the similarity matrix was input into the matching refinement module of the network, where a combination of soft and hard matching was used to generate the point correspondence matrix. Finally, the target point cloud features, source point cloud, and correspondence matrix were input into the shape-aware attention module to obtain the final registration result. With this method, the registration results simultaneously possessed point correspondence and shape similarity with the target point cloud. Experimental results on public and synthetic datasets, as well as visual effects and quantitative comparisons, demonstrated that the proposed method accurately obtained the point correspondence and shape similarity between the source and target point clouds, effectively achieving unsupervised 3D point cloud non-rigid registration.

Key words: point cloud, non-rigid registration, point correspondence, shape-aware attention

中图分类号:

TP391

吴亦奇, 何嘉乐, 张甜甜, 张德军, 李艳丽, 陈壹林. 基于多重特征提取和点对应关系的三维点云非刚配准[J]. 图学学报, 2025, 46(1): 150-158.

WU Yiqi, HE Jiale, ZHANG Tiantian, ZHANG Dejun, LI Yanli, CHEN Yilin. Unsupervised 3D point cloud non-rigid registration based on multi-feature extraction and point correspondence[J]. Journal of Graphics, 2025, 46(1): 150-158.

图/表 11

图1 本文方法网络结构

Fig. 1 The pipeline of the proposed network

图2 特征相似度矩阵匹配精细化流程

Fig. 2 Feature similarity matrix matching refinement

图3 形状感知注意力模块

Fig. 3 Shape-aware attention module

图4 Surreal数据集上点对应关系准确率

Fig. 4 Point correspondence accuracy (Surreal dataset)

图5 配准可视化结果((a)容错率10%；(b)容错率20%)

Fig. 5 Registration visualization ((a) 10% tolerance rate; (b) 20% tolerance rate)

表1 不同容错率下点对应关系准确率/%和CD值

Table 1 Point correspondence accuracy/% and CD results under different tolerance rates

方法	容错率			CD
方法	0%	10%	20%	CD
CPD-Net^[9]	0.33	6.82	24.90	0.004 8
FlowNet3D^[23]	1.21	19.76	41.35	0.004 6
CorrNet3D^[21]	2.05	25.68	48.86	0.002 6
NrtNet^[32]	2.69	30.04	51.88	-
HCDNet3D^[39]	2.53	31.89	54.27	-
本文方法	7.44	38.12	54.89	0.001 3

表2 不同变形率下非刚配准方法的CD对比

Table 2 Comparison of chamfer distance for non-rigid registration methods under different deformation rates

方法	变形率
方法	0.3	0.5	0.7	0.8
CPD-Net^[9]	0.001 4	0.003 0	0.004 2	0.004 7
FlowNet3D^[23]	0.001 6	0.003 4	0.004 3	0.005 2
CorrNet3D^[21]	0.001 5	0.002 9	0.004 1	0.005 1
文献[26]	0.001 4	0.001 8	0.001 8	0.002 7
本文方法	0.001 4	0.001 5	0.001 5	0.001 6

表3 不同变形率下在20%容错率下的点对应关系准确率对比/%

Table 3 Comparison of point correspondence accuracy under 20% tolerance rate at different deformation levels/%

方法	变形率
方法	0.3	0.5	0.7	0.8
CPD-Net^[9]	41.12	33.26	28.34	25.65
FlowNet3D^[23]	94.78	86.51	78.57	76.43
CorrNet3D^[21]	97.67	96.60	94.94	94.07
本文方法	99.54	99.40	98.84	98.43

图6 不同形变率下的配准可视化结果((a) 0.3变形率；(b) 0.5变形率；(c) 0.7变形率；(d) 0.8变形率)

Fig. 6 Registration visualization results under different deformation rates ((a) 0.3 deformation rate; (b) 0.5 deformation rate; (c) 0.7 deformation rate; (d) 0.8 deformation rate)

表4 消融实验设置

Table 4 Ablation experiment setup

方法	多重特征提取	匹配精细化	形状感知注意力
方法A		√	√
方法B	√	√
方法C		√
方法D			√
方法E	√	√	√

表5 消融实验结果/%

Table 5 Ablation experiment results/%

方法	容错率
方法	0%	10%	20%
方法A	19.77	82.33	93.47
方法B	18.07	78.75	90.58
方法C	18.29	74.10	87.07
方法D	16.34	77.23	88.45
方法E	20.21	84.77	94.38

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基于多重特征提取和点对应关系的三维点云非刚配准

Unsupervised 3D point cloud non-rigid registration based on multi-feature extraction and point correspondence

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