A Unified Spatial-Angular Structured Light for Single-View Acquisition of Shape and Reflectance

Xianmin Xu, Yuxin Lin, Haoyang Zhou, Chong Zeng, Yaxin Yu, Kun Zhou, Hongzhi Wu

CVPR 2023 pp. 206-215

doi:10.1109/CVPR52729.2023.00028 /cvpr/2023/xu2023cvpr-unified/

Abstract

We propose a unified structured light, consisting of an LED array and an LCD mask, for high-quality acquisition of both shape and reflectance from a single view. For geometry, one LED projects a set of learned mask patterns to accurately encode spatial information; the decoded results from multiple LEDs are then aggregated to produce a final depth map. For appearance, learned light patterns are cast through a transparent mask to efficiently probe angularly-varying reflectance. Per-point BRDF parameters are differentiably optimized with respect to corresponding measurements, and stored in texture maps as the final reflectance. We establish a differentiable pipeline for the joint capture to automatically optimize both the mask and light patterns towards optimal acquisition quality. The effectiveness of our light is demonstrated with a wide variety of physical objects. Our results compare favorably with state-of-the-art techniques.

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Cite

Text

Xu et al. "A Unified Spatial-Angular Structured Light for Single-View Acquisition of Shape and Reflectance." Conference on Computer Vision and Pattern Recognition, 2023. doi:10.1109/CVPR52729.2023.00028

Markdown

[Xu et al. "A Unified Spatial-Angular Structured Light for Single-View Acquisition of Shape and Reflectance." Conference on Computer Vision and Pattern Recognition, 2023.](https://mlanthology.org/cvpr/2023/xu2023cvpr-unified/) doi:10.1109/CVPR52729.2023.00028

BibTeX

@inproceedings{xu2023cvpr-unified,
  title     = {{A Unified Spatial-Angular Structured Light for Single-View Acquisition of Shape and Reflectance}},
  author    = {Xu, Xianmin and Lin, Yuxin and Zhou, Haoyang and Zeng, Chong and Yu, Yaxin and Zhou, Kun and Wu, Hongzhi},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2023},
  pages     = {206-215},
  doi       = {10.1109/CVPR52729.2023.00028},
  url       = {https://mlanthology.org/cvpr/2023/xu2023cvpr-unified/}
}