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; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 5221-5231

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/

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[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} }