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NeRF-based Polarimetric Multi-view Stereo

文献类型： 外文期刊

作者： Cao, Jiakai 1 ; Yuan, Zhenlong 1 ; Mao, Tianlu 1 ; Wang, Zhaoqi 1 ; Li, Zhaoxin 2 ;

作者机构： 1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China

2.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Agr Big Data, Beijing 100081, Peoples R China

关键词： Multi-view stereo; Neural radiance fields; Shape-from-polarization; 3D reconstruction

期刊名称：PATTERN RECOGNITION （ 影响因子：7.6； 五年影响因子：7.9 ）

ISSN： 0031-3203

年卷期： 2025 年 158 卷

页码：

收录情况： SCI

摘要： In this paper, we introduce NeRF-based Polarimetric Multi-view Stereo (NPMVS), a novel 3D reconstruction method that combines the advantages of neural radiance field (NeRF) and shape-from-polarization (SfP) address the challenge posed by textureless areas while preserving the fine-scale geometric details. Our method first leverages neural rendering to yield depth priors for each input view, subsequently estimates more accurate depths and normals using polarimetric refinement. We further introduce a pixel-wise depth rectification process to address the scaling problem inherent to the polarimetric refinement procedure. In addition, we contribute new realistic pBRDF-based multi-view synthetic dataset, comprised of RGB and polarization images rendered under real-world lighting conditions, which will serve as a valuable resource for future research in this field. Experimental evaluations on both synthetic and real-world datasets validate the superiority of NPMVS, demonstrating its advantage over other state-of-the-art multi-view stereo and shape-from-polarization methods.

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