CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Do We Need Binary Features for 3D Reconstruction?
Bin Fan; Qingqun Kong; Wei Sui; Zhiheng Wang; Xinchao Wang; Shiming Xiang; Chunhong Pan; Pascal Fua
Conference NameConference on Computer Vision and Pattern Recognition
Conference Date2016-6
Conference PlaceLas Vegas, USA
AbstractBinary features have been incrementally popular in the past few years due to their low memory footprints and the efficient computation of Hamming distance between binary descriptors. They have been shown with promising results on some real time applications, e.g., SLAM, where the matching operations are relative few. However, in computer vision, there are many applications such as 3D reconstruction requiring lots of matching operations between local features. Therefore, a natural question is that is the binary feature still a promising solution to this kind of applications? To get the answer, this paper conducts a comparative study of binary features and their matching methods on the context of 3D reconstruction in a recently proposed large scale mutliview stereo dataset. Our evaluations reveal that not all binary features are capable of this task. Most of them are inferior to the classical SIFT based method in terms of reconstruction accuracy and completeness with a not significant better computational performance.
Document Type会议论文
Recommended Citation
GB/T 7714
Bin Fan,Qingqun Kong,Wei Sui,et al. Do We Need Binary Features for 3D Reconstruction?[C],2016.
Files in This Item: Download All
File Name/Size DocType Version Access License
07789634.pdf(265KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Bin Fan]'s Articles
[Qingqun Kong]'s Articles
[Wei Sui]'s Articles
Baidu academic
Similar articles in Baidu academic
[Bin Fan]'s Articles
[Qingqun Kong]'s Articles
[Wei Sui]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Bin Fan]'s Articles
[Qingqun Kong]'s Articles
[Wei Sui]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 07789634.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.