Point cloud registration method based on coordinate geometric sampling

doi:10.11772/j.issn.1001-9081.2024010045

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (1): 214-222.DOI: 10.11772/j.issn.1001-9081.2024010045

• Multimedia computing and computer simulation • Previous Articles Next Articles

Point cloud registration method based on coordinate geometric sampling

Jietao LIANG¹, Bing LUO¹, Lanhui FU¹, Qingling CHANG¹, Nannan LI², Ningbo YI³, Qi FENG⁴, Xin HE⁴, Fuqin DENG¹()

^1.School of Electronics and Information Engineering，Wuyi University，Jiangmen Guangdong 529020，China
^2.Faculty of Innovative Engineering，Macau University of Science and Technology，Macau 999078，China
^3.College of Textile Science and Engineering，Wuyi University，Jiangmen Guangdong 529020，China
^4.School of Applied Physics and Materials，Wuyi University，Jiangmen Guangdong 529020，China

Received:2024-01-17 Revised:2024-04-15 Accepted:2024-04-15 Online:2024-05-09 Published:2025-01-10
Contact: Fuqin DENG
About author:LIANG Jietao， born in 1999， M. S. candidate. His research interests include point cloud registration.
LUO Bing， born in 1966， Ph. D.， professor. His research interests include digital image processing and application， artificial intelligence.
FU Lanhui， born in 1987， Ph. D.， lecturer. Her research interests include machine learning， image information processing.
CHANG Qingling， born in 1984， Ph. D.， associate professor. Her research interests include artificial intelligence， natural language processing， knowledge graph， computer vision.
LI Nannan， born in 1982， Ph. D.， assistant professor. His research interests include computer vision.
YI Ningbo， born in 1988， Ph. D.， associate professor. His research interests include advanced semiconductor materials （graphene， perovskite）.
FENG Qi， born in 1990， Ph. D.， lecturer. His research interests include electrochemical energy system.
HE Xin， born in 1981， Ph. D.， professor. Her research interests include flexible transparent electrode and its application in photoelectric devices.
Supported by:
National Natural Science Foundation of China(62073274);Jiangmen Science and Technology Project(2020030103000008537);Wuyi University-Hong Kong-Macau Joint Funding Scheme(2022WGALH17);Exploratory Research Project of Shenzhen Institute of Artificial Intelligence and Robotics for Society(AC01202101103)

基于坐标几何采样的点云配准方法

梁杰涛¹, 罗兵¹, 付兰慧¹, 常青玲¹, 李楠楠², 易宁波³, 冯其⁴, 何鑫⁴, 邓辅秦¹()

^1.五邑大学电子与信息工程学院，广东江门 529020
^2.澳门科技大学创新工程学院，澳门 999078
^3.五邑大学纺织科学与工程学院，广东江门 529020
^4.五邑大学应用物理与材料学院，广东江门 529020

通讯作者: 邓辅秦
作者简介:梁杰涛（1999—），男，广东江门人，硕士研究生，主要研究方向：点云配准；
罗兵（1966—），男，湖北荆州人，教授，博士，主要研究方向：数字图像处理及应用、人工智能；
付兰慧（1987—），女，河南新乡人，讲师，博士，主要研究方向：机器学习、图像信息处理；
常青玲（1984—），女，河南许昌人，副教授，博士，主要研究方向：人工智能、自然语言处理、知识图谱、计算机视觉；
李楠楠（1982—），男，安徽阜阳人，助理教授，博士，主要研究方向：机器视觉；
易宁波（1988—），男，四川泸州人，副教授，博士，主要研究方向：先进半导体材料（石墨烯、钙钛矿）；
冯其（1990—），男，湖南湘潭人，讲师，博士，主要研究方向：电化学能源系统；
何鑫（1981—），女，湖南衡阳人，教授，博士，主要研究方向：柔性透明电极及其在光电器件中的应用；
基金资助:
国家自然科学基金资助项目(62073274);江门科技计划项目(2020030103000008537);五邑大学港澳联合基金资助项目(2022WGALH17);深圳市人工智能与机器人研究院探索性研究项目(AC01202101103)

Abstract

Abstract:

To improve accuracy， robustness， and generalization of point cloud registration and address the problem of the Iterative Closest Point （ICP） algorithm easily falling into local optimal solution， a point cloud registration method of coordinate Geometric Sampling based on Deep Closest Point （GSDCP） was proposed. Firstly， the central point curvature was estimated using coordinates of surrounding points of each point， and points that preserved geometric features of the point cloud were selected through curvature sizes， so as to realize downsampling of the point cloud. Secondly， a Dynamic Graph Convolutional Neural Network （DGCNN） was employed to coordinate with the downsampled point cloud to learn point cloud features that incorporated local geometry information， and contextual information was captured using a Transformer， and soft Pointers facilitate approximate combination and matching between two feature embedders. Finally， a differentiable Single Value Decomposition （SVD） layer was utilized to estimate the final rigid transformation. Point cloud registration experimental results on ModelNet40 dataset show that compared with ICP， Globally optimal ICP （Go-ICP）， PointNetLK， Fast Global Registration （FGR）， ADGCNNLK （Attention Dynamic Graph Convolutional Neural Network Lucas-Kanade）， Deep Closest Point （DCP）， and Multi-Features Guidance Network （MFGNet）， GSDCP achieves all the best registration accuracy and robustness in scenarios with or without noise， as well as when the point cloud category is invisible. In noise-free scenario， GSDCP reduces rotational Mean Square Error （MSE） by 31.3% and translational MSE by 58.3% compared to MFGNet. In noisy scenario， GSDCP reduces rotational MSE by 33.9% and translational MSE by 73.4% compared to MFGNet. When the point cloud category is invisible， GSDCP reduces rotational MSE by 57.7% and translational MSE by 77.9% compared to MFGNet. Additionally， when dealing with incomplete point cloud data （including random occlusion and fragmentary point cloud）， GSDCP exhibits reductions of 35.1% in rotational MSE and 39.8% in translational MSE compared to MFGNet when point cloud integrity is below 75%.

Key words: point cloud registration, deep learning, geometric sampling, feature extraction, Transformer

摘要：

为了提高点云配准的精度、鲁棒性和泛化性，解决迭代最近点（ICP）算法容易陷入局部最优解的问题，提出一种基于坐标几何采样的深度最近点（GSDCP）的点云配准方法。首先，基于每个点的周围点的坐标估计中心点曲率，并通过曲率大小筛选出能保留点云几何特征的点，从而完成点云下采样；然后，使用动态图卷积神经网络（DGCNN）配合下采样点云学习融入局部几何信息的点云特征，并通过Transformer捕获两个特征嵌入之间的上下文信息、使用软指针近似组合匹配；最后，利用一个可微的奇异值分解（SVD）层估计最终的刚性变换。在数据集ModelNet40上进行的点云配准实验结果表明，与ICP、Go-ICP （Globally optimal ICP）、PointNetLK、快速全局配准（FGR）、ADGCNNLK （Attention Dynamic Graph Convolutional Neural Network Lucas-Kanade）、深度最近点（DCP）和多特征引导网络（MFGNet）相比，在无噪声、有噪声和看不见点云类别的情况下GSDCP的配准精度和鲁棒性都最好；其中在无噪声的情况下，与MFGNet相比，GSDCP的旋转均方误差（MSE）降低了31.3%，平移MSE降低了58.3%；在有噪声的情况下， GSDCP的旋转MSE降低了33.9%，平移MSE降低了73.4%；在看不见点云类别的情况下， GSDCP的旋转MSE降低了57.7%，平移MSE降低了77.9%。除此之外，对不完整点云数据（包括随机遮挡和点云残缺），在点云完整度为75%以下时， GSDCP的旋转MSE降低了35.1%，平移MSE降低了39.8%。

关键词: 点云配准, 深度学习, 几何采样, 特征提取, Transformer

CLC Number:

TP391.41

Jietao LIANG, Bing LUO, Lanhui FU, Qingling CHANG, Nannan LI, Ningbo YI, Qi FENG, Xin HE, Fuqin DENG. Point cloud registration method based on coordinate geometric sampling[J]. Journal of Computer Applications, 2025, 45(1): 214-222.

梁杰涛, 罗兵, 付兰慧, 常青玲, 李楠楠, 易宁波, 冯其, 何鑫, 邓辅秦. 基于坐标几何采样的点云配准方法[J]. 《计算机应用》唯一官方网站, 2025, 45(1): 214-222.

Figures/Tables 10

References 44

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模块	MSE（R）	RMSE（R）	MAE（R）	MSE（t）	RMSE（t）	MAE（t）
ICP	894.897 339	29.914 835	23.544 817	0.084 643	0.084 643	0.248 755
Go-ICP	140.477 325	11.852 313	2.588 463	0.000 659	0.025 665	0.007 092
FGR	87.661 491	9.362 772	1.999 290	0.000 194	0.013 939	0.002 839
PointNetLK	227.870 331	15.095 374	4.225 304	0.000 487	0.022 065	0.005 404
ADGCNNLK	61.045 283	7.813 148	2.854 556	0.000 283	0.016 822	0.004 146
DCP	7.960 885	2.821 504	1.920 297	0.000 445	0.021 104	0.013 548
MFGNet	2.829 443	1.682 095	1.134 576	0.000 283	0.016 835	0.002 513
GSDCP	1.944 644	1.394 505	0.981 491	0.000 118	0.010 864	0.001 986

模块	MSE（R）	RMSE（R）	MAE（R）	MSE（t）	RMSE（t）	MAE（t）
ICP	894.897 339	29.914 835	23.544 817	0.084 643	0.084 643	0.248 755
Go-ICP	140.477 325	11.852 313	2.588 463	0.000 659	0.025 665	0.007 092
FGR	87.661 491	9.362 772	1.999 290	0.000 194	0.013 939	0.002 839
PointNetLK	227.870 331	15.095 374	4.225 304	0.000 487	0.022 065	0.005 404
ADGCNNLK	61.045 283	7.813 148	2.854 556	0.000 283	0.016 822	0.004 146
DCP	7.960 885	2.821 504	1.920 297	0.000 445	0.021 104	0.013 548
MFGNet	2.829 443	1.682 095	1.134 576	0.000 283	0.016 835	0.002 513
GSDCP	1.944 644	1.394 505	0.981 491	0.000 118	0.010 864	0.001 986

模块	MSE（R）	RMSE（R）	MAE（R）	MSE（t）	RMSE（t）	MAE（t）
ICP	892.601 135	29.876 431	23.626 110	0.086 005	0.293 266	0.251 916
Go-ICP	192.258 636	13.865 736	2.914 169	0.000 491	0.022 154	0.006 219
FGR	97.002 747	9.848 997	1.445 460	0.000 182	0.013 503	0.002 531
PointNetLK	306.323 975	17.502 113	5.280 545	0.000 784	0.028 007	0.007 203
ADGCNNLK	84.145 566	9.173 089	3.155 154	0.000 454	0.021 307	0.005 651
DCP	10.030 007	3.167 019	2.014 827	0.000 520	0.022 814	0.013 855
MFGNet	3.941 372	1.959 942	1.539 593	0.000 420	0.020 518	0.003 482
GSDCP	1.667 429	1.291 290	0.919 080	0.000 093	0.009 659	0.002 361

模块	MSE（R）	RMSE（R）	MAE（R）	MSE（t）	RMSE（t）	MAE（t）
ICP	892.601 135	29.876 431	23.626 110	0.086 005	0.293 266	0.251 916
Go-ICP	192.258 636	13.865 736	2.914 169	0.000 491	0.022 154	0.006 219
FGR	97.002 747	9.848 997	1.445 460	0.000 182	0.013 503	0.002 531
PointNetLK	306.323 975	17.502 113	5.280 545	0.000 784	0.028 007	0.007 203
ADGCNNLK	84.145 566	9.173 089	3.155 154	0.000 454	0.021 307	0.005 651
DCP	10.030 007	3.167 019	2.014 827	0.000 520	0.022 814	0.013 855
MFGNet	3.941 372	1.959 942	1.539 593	0.000 420	0.020 518	0.003 482
GSDCP	1.667 429	1.291 290	0.919 080	0.000 093	0.009 659	0.002 361

模块	MSE（R）	RMSE（R）	MAE（R）	MSE（t）	RMSE（t）	MAE（t）
ICP	882.564 209	29.707 983	23.557 217	0.084 537	0.290 752	0.249 092
Go-ICP	131.182 495	11.453 493	2.534 873	0.000 531	0.023 051	0.004 192
FGR	607.694 885	24.651 468	10.055 918	0.011 876	0.108 977	0.027 393
PointNetLK	256.155 548	16.004 860	4.595 617	0.000 465	0.021 558	0.005 652
ADGCNNLK	70.512 636	8.397 180	2.559 214	0.000 278	0.016 673	0.004 526
DCP	48.509 663	6.964 888	4.855 263	0.003 790	0.061 565	0.043 154
MFGNet	19.317 484	4.395 166	3.610 706	0.000 847	0.029 113	0.009 011
GSDCP	12.772 079	3.573 805	2.557 062	0.000 225	0.015 000	0.001 812

Point cloud registration method based on coordinate geometric sampling

基于坐标几何采样的点云配准方法

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Metrics

模块	MSE（R）	RMSE（R）	MAE（R）	MSE（t）	RMSE（t）	MAE（t）
FPS+PointNet	76.249 426	8.732 091	5.929 955	0.005 915	0.076 911	0.053 945
GS+PointNet	51.354 679	7.166 218	4.514 465	0.003 403	0.058 338	0.037 922
FPS+DGCNN	7.960 885	2.821 504	1.920 297	0.000 445	0.021 104	0.013 548
GS+DGCNN	1.944 644	1.394 505	0.981 491	0.000 118	0.010 864	0.001 986

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