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Polarimetric Helmholtz Stereopsis

Yuqi Ding¹, Yu Ji², Mingyuan Zhou, Sing Bing Kang³, Jinwei Ye^1,4,

¹Louisiana State University, ²Tencent Pixel Lab, ³Zillow Group, ⁴George Mason University

TPAMI Ver. ICCV Ver. Video Data Supplementary Material

Abstract

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In this paper, we present the polarimetric Helmholtz stereopsis (polar-HS), which extends the classical Helmholtz stereopsis by considering the polarization state of light in the reciprocal paths. With the additional phase information from polarization, polar-HS requires only one reciprocal image pair. We formulate new reciprocity and diffuse/specular polarimetric constraints to recover surface depths and normals using an optimization framework. Using a hardware prototype, we show that our approach produces high-quality 3D reconstruction for different types of surfaces, ranging from diffuse to highly specular.

3D Reconstruction Results

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Polarimetric Image Decomposition

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BibTeX

@ARTICLE {ding2024phs_pami,
  author    = {Ding, Yuqi and Ji, Yu and Cheng, Zhang and Zhou, Mingyuan and Kang, Sing Bing and Ye, Jinwei},
  title     = {Polarimetric Helmholtz Stereopsis},
  journal   = {IEEE Transactions on Pattern Analysis & Machine Intelligence},
  month     = {January},
  year      = {2024},
}

@InProceedings{ding2021phs_iccv,
  author    = {Ding, Yuqi and Ji, Yu and Zhou, Mingyuan and Kang, Sing Bing and Ye, Jinwei},
  title     = {Polarimetric Helmholtz Stereopsis},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  month     = {October},
  year      = {2021},
}