Efficient 3D Object Recognition from Cluttered Point Cloud
Recognizing 3D objects and estimating their postures in a complex scene is a challenging task. Sample Consensus Initial Alignment (SAC-IA) is a commonly used point cloud-based method to achieve such a goal. However, its efficiency is low, and it cannot be applied in real-time applications. This pape...
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doaj-286613bd2b014d9c8f406bd196b3d60f2021-09-09T13:56:30ZengMDPI AGSensors1424-82202021-08-01215850585010.3390/s21175850Efficient 3D Object Recognition from Cluttered Point CloudWei Li0Hongtai Cheng1Xiaohua Zhang2Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Mechanical of Engineering and Automation, Northeastern University, Shenyang 110167, ChinaFaculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, ChinaRecognizing 3D objects and estimating their postures in a complex scene is a challenging task. Sample Consensus Initial Alignment (SAC-IA) is a commonly used point cloud-based method to achieve such a goal. However, its efficiency is low, and it cannot be applied in real-time applications. This paper analyzes the most time-consuming part of the SAC-IA algorithm: sample generation and evaluation. We propose two improvements to increase efficiency. In the initial aligning stage, instead of sampling the key points, the correspondence pairs between model and scene key points are generated in advance and chosen in each iteration, which reduces the redundant correspondence search operations; a geometric filter is proposed to prevent the invalid samples to the evaluation process, which is the most time-consuming operation because it requires transforming and calculating the distance between two point clouds. The introduction of the geometric filter can significantly increase the sample quality and reduce the required sample numbers. Experiments are performed on our own datasets captured by Kinect v2 Camera and on Bologna 1 dataset. The results show that the proposed method can significantly increase (10–30×) the efficiency of the original SAC-IA method without sacrificing accuracy.https://www.mdpi.com/1424-8220/21/17/5850object recognitionpoint cloudSAC-IARANSAC |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wei Li Hongtai Cheng Xiaohua Zhang |
spellingShingle |
Wei Li Hongtai Cheng Xiaohua Zhang Efficient 3D Object Recognition from Cluttered Point Cloud Sensors object recognition point cloud SAC-IA RANSAC |
author_facet |
Wei Li Hongtai Cheng Xiaohua Zhang |
author_sort |
Wei Li |
title |
Efficient 3D Object Recognition from Cluttered Point Cloud |
title_short |
Efficient 3D Object Recognition from Cluttered Point Cloud |
title_full |
Efficient 3D Object Recognition from Cluttered Point Cloud |
title_fullStr |
Efficient 3D Object Recognition from Cluttered Point Cloud |
title_full_unstemmed |
Efficient 3D Object Recognition from Cluttered Point Cloud |
title_sort |
efficient 3d object recognition from cluttered point cloud |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-08-01 |
description |
Recognizing 3D objects and estimating their postures in a complex scene is a challenging task. Sample Consensus Initial Alignment (SAC-IA) is a commonly used point cloud-based method to achieve such a goal. However, its efficiency is low, and it cannot be applied in real-time applications. This paper analyzes the most time-consuming part of the SAC-IA algorithm: sample generation and evaluation. We propose two improvements to increase efficiency. In the initial aligning stage, instead of sampling the key points, the correspondence pairs between model and scene key points are generated in advance and chosen in each iteration, which reduces the redundant correspondence search operations; a geometric filter is proposed to prevent the invalid samples to the evaluation process, which is the most time-consuming operation because it requires transforming and calculating the distance between two point clouds. The introduction of the geometric filter can significantly increase the sample quality and reduce the required sample numbers. Experiments are performed on our own datasets captured by Kinect v2 Camera and on Bologna 1 dataset. The results show that the proposed method can significantly increase (10–30×) the efficiency of the original SAC-IA method without sacrificing accuracy. |
topic |
object recognition point cloud SAC-IA RANSAC |
url |
https://www.mdpi.com/1424-8220/21/17/5850 |
work_keys_str_mv |
AT weili efficient3dobjectrecognitionfromclutteredpointcloud AT hongtaicheng efficient3dobjectrecognitionfromclutteredpointcloud AT xiaohuazhang efficient3dobjectrecognitionfromclutteredpointcloud |
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