PPTFH: Robust Local Descriptor Based on Point-Pair Transformation Features for 3D Surface Matching
Three-dimensional feature description for a local surface is a core technology in 3D computer vision. Existing descriptors perform poorly in terms of distinctiveness and robustness owing to noise, mesh decimation, clutter, and occlusion in real scenes. In this paper, we propose a 3D local surface de...
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MDPI AG
2021-05-01
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Online Access: | https://www.mdpi.com/1424-8220/21/9/3229 |
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author | Lang Wu Kai Zhong Zhongwei Li Ming Zhou Hongbin Hu Congjun Wang Yusheng Shi |
author_facet | Lang Wu Kai Zhong Zhongwei Li Ming Zhou Hongbin Hu Congjun Wang Yusheng Shi |
author_sort | Lang Wu |
collection | DOAJ |
description | Three-dimensional feature description for a local surface is a core technology in 3D computer vision. Existing descriptors perform poorly in terms of distinctiveness and robustness owing to noise, mesh decimation, clutter, and occlusion in real scenes. In this paper, we propose a 3D local surface descriptor using point-pair transformation feature histograms (PPTFHs) to address these challenges. The generation process of the PPTFH descriptor consists of three steps. First, a simple but efficient strategy is introduced to partition the point-pair sets on the local surface into four subsets. Then, three feature histograms corresponding to each point-pair subset are generated by the point-pair transformation features, which are computed using the proposed Darboux frame. Finally, all the feature histograms of the four subsets are concatenated into a vector to generate the overall PPTFH descriptor. The performance of the PPTFH descriptor is evaluated on several popular benchmark datasets, and the results demonstrate that the PPTFH descriptor achieves superior performance in terms of descriptiveness and robustness compared with state-of-the-art algorithms. The benefits of the PPTFH descriptor for 3D surface matching are demonstrated by the results obtained from five benchmark datasets. |
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issn | 1424-8220 |
language | English |
last_indexed | 2024-03-10T11:39:30Z |
publishDate | 2021-05-01 |
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series | Sensors |
spelling | doaj.art-bd0c3e0e593f4cf4aa82f7746502e1702023-11-21T18:37:56ZengMDPI AGSensors1424-82202021-05-01219322910.3390/s21093229PPTFH: Robust Local Descriptor Based on Point-Pair Transformation Features for 3D Surface MatchingLang Wu0Kai Zhong1Zhongwei Li2Ming Zhou3Hongbin Hu4Congjun Wang5Yusheng Shi6State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaHubei Tri-Ring Forging Co., Ltd., Xiangyang 441700, ChinaHubei Tri-Ring Forging Co., Ltd., Xiangyang 441700, ChinaState Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaThree-dimensional feature description for a local surface is a core technology in 3D computer vision. Existing descriptors perform poorly in terms of distinctiveness and robustness owing to noise, mesh decimation, clutter, and occlusion in real scenes. In this paper, we propose a 3D local surface descriptor using point-pair transformation feature histograms (PPTFHs) to address these challenges. The generation process of the PPTFH descriptor consists of three steps. First, a simple but efficient strategy is introduced to partition the point-pair sets on the local surface into four subsets. Then, three feature histograms corresponding to each point-pair subset are generated by the point-pair transformation features, which are computed using the proposed Darboux frame. Finally, all the feature histograms of the four subsets are concatenated into a vector to generate the overall PPTFH descriptor. The performance of the PPTFH descriptor is evaluated on several popular benchmark datasets, and the results demonstrate that the PPTFH descriptor achieves superior performance in terms of descriptiveness and robustness compared with state-of-the-art algorithms. The benefits of the PPTFH descriptor for 3D surface matching are demonstrated by the results obtained from five benchmark datasets.https://www.mdpi.com/1424-8220/21/9/3229local surface descriptor3D surface matchingobject recognition3D registration |
spellingShingle | Lang Wu Kai Zhong Zhongwei Li Ming Zhou Hongbin Hu Congjun Wang Yusheng Shi PPTFH: Robust Local Descriptor Based on Point-Pair Transformation Features for 3D Surface Matching Sensors local surface descriptor 3D surface matching object recognition 3D registration |
title | PPTFH: Robust Local Descriptor Based on Point-Pair Transformation Features for 3D Surface Matching |
title_full | PPTFH: Robust Local Descriptor Based on Point-Pair Transformation Features for 3D Surface Matching |
title_fullStr | PPTFH: Robust Local Descriptor Based on Point-Pair Transformation Features for 3D Surface Matching |
title_full_unstemmed | PPTFH: Robust Local Descriptor Based on Point-Pair Transformation Features for 3D Surface Matching |
title_short | PPTFH: Robust Local Descriptor Based on Point-Pair Transformation Features for 3D Surface Matching |
title_sort | pptfh robust local descriptor based on point pair transformation features for 3d surface matching |
topic | local surface descriptor 3D surface matching object recognition 3D registration |
url | https://www.mdpi.com/1424-8220/21/9/3229 |
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