Knowledge Commons of Institute of Automation,CAS
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 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
ISSN | 1057-7149 |
2019-10-01 | |
卷号 | 28期号:10页码:4774-4789 |
产权排序 | 1 |
摘要 | 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. |
关键词 | Local feature image reconstruction structure from motion (SFM) 3D vision image matching |
DOI | 10.1109/TIP.2019.2909640 |
关键词[WOS] | DESCRIPTORS ; BINARY ; SCALE ; DETECTORS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Young Elite Scientists Sponsorship Program by CAST[2018QNRC001] ; National Natural Science Foundation of China[61876180] ; Henan University Scientific and Technological Innovation Team Support Program[19IRTSTHN012] ; Henan Science and Technology Innovation Outstanding Youth Program[184100510009] ; National Natural Science Foundation of China[61573352] ; National 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研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000480312800005 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 三维视觉 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/27551 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
通讯作者 | Wang, Zhiheng |
作者单位 | 1.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 |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 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. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Feat-TIP19.pdf(3986KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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