Bayesian Depth-from-Defocus with Shading Constraints

Chen Li, Shuochen Su, Yasuyuki Matsushita, Kun Zhou, Stephen Lin
{"title":"Bayesian Depth-from-Defocus with Shading Constraints","authors":"Chen Li, Shuochen Su, Yasuyuki Matsushita, Kun Zhou, Stephen Lin","doi":"10.1109/CVPR.2013.35","DOIUrl":null,"url":null,"abstract":"We present a method that enhances the performance of depth-from-defocus (DFD) through the use of shading information. DFD suffers from important limitations - namely coarse shape reconstruction and poor accuracy on texture less surfaces - that can be overcome with the help of shading. We integrate both forms of data within a Bayesian framework that capitalizes on their relative strengths. Shading data, however, is challenging to recover accurately from surfaces that contain texture. To address this issue, we propose an iterative technique that utilizes depth information to improve shading estimation, which in turn is used to elevate depth estimation in the presence of textures. With this approach, we demonstrate improvements over existing DFD techniques, as well as effective shape reconstruction of texture less surfaces.","PeriodicalId":6343,"journal":{"name":"2013 IEEE Conference on Computer Vision and Pattern Recognition","volume":"22 1 1","pages":"217-224"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Conference on Computer Vision and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPR.2013.35","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

We present a method that enhances the performance of depth-from-defocus (DFD) through the use of shading information. DFD suffers from important limitations - namely coarse shape reconstruction and poor accuracy on texture less surfaces - that can be overcome with the help of shading. We integrate both forms of data within a Bayesian framework that capitalizes on their relative strengths. Shading data, however, is challenging to recover accurately from surfaces that contain texture. To address this issue, we propose an iterative technique that utilizes depth information to improve shading estimation, which in turn is used to elevate depth estimation in the presence of textures. With this approach, we demonstrate improvements over existing DFD techniques, as well as effective shape reconstruction of texture less surfaces.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
具有阴影约束的贝叶斯离焦深度
我们提出了一种通过使用阴影信息来增强离焦深度(DFD)性能的方法。DFD有一些重要的限制,即粗糙的形状重建和纹理较少的表面上的精度差,这些可以通过阴影的帮助来克服。我们将两种形式的数据集成在一个贝叶斯框架中,利用它们的相对优势。然而,从包含纹理的表面精确恢复阴影数据是具有挑战性的。为了解决这个问题,我们提出了一种迭代技术,利用深度信息来改进阴影估计,从而提高纹理存在时的深度估计。通过这种方法,我们展示了对现有DFD技术的改进,以及对无纹理表面的有效形状重建。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Segment-Tree Based Cost Aggregation for Stereo Matching Event Retrieval in Large Video Collections with Circulant Temporal Encoding Articulated and Restricted Motion Subspaces and Their Signatures Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation Learning Video Saliency from Human Gaze Using Candidate Selection
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1