OpenSubstance: A High-Quality Measured Dataset of Multi-View and -Lighting Images and Shapes

Abstract

We present OpenSubstance, a high-quality measured dataset with 2.4 million high-dynamic-range images of 187 objects with a wide variety in shape and appearance, captured under 270 camera views and 1,637 lighting conditions, including 1,620 one-light-at-a-time, 8 environment, 8 linear and 1 full-on illumination. For each image, the corresponding lighting condition, camera parameters and foreground segmentation mask are provided. High-precision 3D geometry is also acquired for rigid objects. It takes 1 hour on average to capture one object with our custom-built high-performance lightstage and a top-grade commercial 3D scanner. We perform comprehensive quantitative evaluation on state-of-the-art techniques across different tasks, including single- and multi-view photometric stereo, as well as relighting. The project is publicly available at https://opensubstance.github.io/

Cite

Text

Pei et al. "OpenSubstance: A High-Quality Measured Dataset of Multi-View and -Lighting Images and Shapes." International Conference on Computer Vision, 2025.

Markdown

[Pei et al. "OpenSubstance: A High-Quality Measured Dataset of Multi-View and -Lighting Images and Shapes." International Conference on Computer Vision, 2025.](https://mlanthology.org/iccv/2025/pei2025iccv-opensubstance/)

BibTeX

@inproceedings{pei2025iccv-opensubstance,
  title     = {{OpenSubstance: A High-Quality Measured Dataset of Multi-View and -Lighting Images and Shapes}},
  author    = {Pei, Fan and Bai, Jinchen and Feng, Xiang and Bi, Zoubin and Zhou, Kun and Wu, Hongzhi},
  booktitle = {International Conference on Computer Vision},
  year      = {2025},
  pages     = {5221-5231},
  url       = {https://mlanthology.org/iccv/2025/pei2025iccv-opensubstance/}
}