OpenSubstance: A High-quality Measured Dataset of Multi-View and -Lighting Images and Shapes
Fan Pei, Jinchen Bai, Xiang Feng, Zoubin Bi, Kun Zhou, Hongzhi Wu
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/
BibTeX
@InProceedings{Pei_2025_ICCV,
author = {Pei, Fan and Bai, Jinchen and Feng, Xiang and Bi, Zoubin and Zhou, Kun and Wu, Hongzhi},
title = {OpenSubstance: A High-quality Measured Dataset of Multi-View and -Lighting Images and Shapes},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5221-5231}
}