A Performance Evaluation of Local Features for Image-Based 3D Reconstruction
Fan, Bin1; Kong, Qingqun2; Wang, Xinchao3,4; Wang, Zhiheng5; Xiang, Shiming1; Pan, Chunhong1; Fua, Pascal6
Contribution Rank1

This paper performs a comprehensive and comparative evaluation of the state-of-the-art local features for the task of image-based 3D reconstruction. The evaluated local features cover the recently developed ones by using powerful machine learning techniques and the elaborately designed handcrafted features. To obtain a comprehensive evaluation, we choose to include both float type features and binary ones. Meanwhile, two kinds of datasets have been used in this evaluation. One is a dataset of many different scene types with groundtruth 3D points, containing images of different scenes captured at fixed positions, for quantitative performance evaluation of different local features in the controlled image capturing situation. The other dataset contains Internet scale image sets of several landmarks with a lot of unrelated images, which is used for qualitative performance evaluation of different local features in the free image collection situation. Our experimental results show that binary features are competent to reconstruct scenes from controlled image sequences with only a fraction of processing time compared to using float type features. However, for the case of a large scale image set with many distracting images, float type features show a clear advantage over binary ones. Currently, the most traditional SIFT is very stable with regard to scene types in this specific task and produces very competitive reconstruction results among all the evaluated local features. Meanwhile, although the learned binary features are not as competitive as the handcrafted ones, learning float type features with CNN is promising but still requires much effort in the future.

KeywordLocal feature image reconstruction structure from motion (SFM) 3D vision image matching
Indexed BySCI
Funding ProjectNational Natural Science Foundation of China[61573352] ; Henan Science and Technology Innovation Outstanding Youth Program[184100510009] ; Henan University Scientific and Technological Innovation Team Support Program[19IRTSTHN012] ; National Natural Science Foundation of China[61876180] ; Young Elite Scientists Sponsorship Program by CAST[2018QNRC001]
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000480312800005
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Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorWang, Zhiheng
Affiliation1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Stevens Inst Technol, Dept Comp Sci, Hoboken, NJ 07030 USA
5.Henan Polytech Univ, Sch Comp Sci & Tech, Jiaozuo 454000, Henan, Peoples R China
6.Ecole Polytech Fed Lausanne, CVLab, CH-1015 Lausanne, Switzerland
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Fan, Bin,Kong, Qingqun,Wang, Xinchao,et al. A Performance Evaluation of Local Features for Image-Based 3D Reconstruction[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(10):4774-4789.
APA Fan, Bin.,Kong, Qingqun.,Wang, Xinchao.,Wang, Zhiheng.,Xiang, Shiming.,...&Fua, Pascal.(2019).A Performance Evaluation of Local Features for Image-Based 3D Reconstruction.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(10),4774-4789.
MLA Fan, Bin,et al."A Performance Evaluation of Local Features for Image-Based 3D Reconstruction".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.10(2019):4774-4789.
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