CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 复杂系统研究
Efficient Confidence-Based Hierarchical Stereo Disparity Upsampling for Noisy Inputs
Xiang-bing Meng; Mei Zhang; Zhao-xing Zhang; Rong Wang; Zheng Geng; Fei-Yue Wang
Source PublicationIEEE Access
2019
Volume0Issue:0Pages:0
AbstractDisparity upsampling techniques aim to restore high-resolution disparity maps from lowresolution disparity inputs. These inputs must be of high quality and are often obtained via complicated passive or active 3D reconstruction methods. Each pixel in the input disparity maps guides the disparity assignment in the upsampling process. The quality of the upsampled results will decrease if the initial disparity inputs are noisy, as the upsampled results are closely related to the initial inputs.We herein propose a hierarchical confidence-based upsampling framework that can be used to obtain relatively high quality upsampled results even under the noisy inputs. Specifically designed confidence measuring schemes are
employed in our upsampling process, allowing the disparity assignment of only high-confidence pixels. For an effective depth quality evaluation, we present a novel classification of the confidence according to depth- and texture-related information and develop a confidence examination method with improved precision by combining multiple depth confidence evaluation methods. Our hierarchical pipeline contains 3 steps: confidence-based upsampling, confidence-based fine-tuning and confidence-based optimization. The upsampling combines multichannel information. Fine-tuning is carried out using the stereo texture information. Optimization is conducted utilizing the Markov random field method. All these proposed methods work together to suppress the low-confidence pixels and propagate the high-confidence pixels in the upsampling process. The cumulative error distribution is further analyzed, revealing the effectiveness of our confidence evaluation. Extensive comparison experiments are also performed using both the ground truth and stereo matching disparity maps as inputs to demonstrate the advantage of our framework over state-of-the-art upsampling methods.
Other Abstract
KeywordDisparity Upsampling Confidence Evaluation Noise Hierarchical Structure Multichannel Upsampling
Indexed BySCI
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21735
Collection复杂系统管理与控制国家重点实验室_复杂系统研究
Corresponding AuthorMei Zhang
Recommended Citation
GB/T 7714
Xiang-bing Meng,Mei Zhang,Zhao-xing Zhang,et al. Efficient Confidence-Based Hierarchical Stereo Disparity Upsampling for Noisy Inputs[J]. IEEE Access,2019,0(0):0.
APA Xiang-bing Meng,Mei Zhang,Zhao-xing Zhang,Rong Wang,Zheng Geng,&Fei-Yue Wang.(2019).Efficient Confidence-Based Hierarchical Stereo Disparity Upsampling for Noisy Inputs.IEEE Access,0(0),0.
MLA Xiang-bing Meng,et al."Efficient Confidence-Based Hierarchical Stereo Disparity Upsampling for Noisy Inputs".IEEE Access 0.0(2019):0.
Files in This Item: Download All
File Name/Size DocType Version Access License
FINAL Article_mxb.pd(3168KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xiang-bing Meng]'s Articles
[Mei Zhang]'s Articles
[Zhao-xing Zhang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xiang-bing Meng]'s Articles
[Mei Zhang]'s Articles
[Zhao-xing Zhang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xiang-bing Meng]'s Articles
[Mei Zhang]'s Articles
[Zhao-xing Zhang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: FINAL Article_mxb.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

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