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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Main Authors: Lang Wu, Kai Zhong, Zhongwei Li, Ming Zhou, Hongbin Hu, Congjun Wang, Yusheng Shi
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Sensors
Subjects:
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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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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AT kaizhong pptfhrobustlocaldescriptorbasedonpointpairtransformationfeaturesfor3dsurfacematching
AT zhongweili pptfhrobustlocaldescriptorbasedonpointpairtransformationfeaturesfor3dsurfacematching
AT mingzhou pptfhrobustlocaldescriptorbasedonpointpairtransformationfeaturesfor3dsurfacematching
AT hongbinhu pptfhrobustlocaldescriptorbasedonpointpairtransformationfeaturesfor3dsurfacematching
AT congjunwang pptfhrobustlocaldescriptorbasedonpointpairtransformationfeaturesfor3dsurfacematching
AT yushengshi pptfhrobustlocaldescriptorbasedonpointpairtransformationfeaturesfor3dsurfacematching