RoboCloth: 500 real cloth materials, 80B observations for neural reflectance reconstruction
taiyasaki · x · 2026-10-20
Researchers from SFU, Westlake University and collaborators released RoboCloth, a large-scale dataset of 500 real cloth materials to break neural materials' long-standing reliance on 100 BRDFs measured at MERL in 2003.
- Each material is imaged in 580 HDR frames spanning a wide view-light hemisphere — 80 billion observations total, with robotically controlled camera and lighting
- A two-stage pipeline: Stage 1 trains a shared MLP BRDF decoder to learn cloth material priors; Stage 2 freezes it and fits a dense 2048×2048 latent texture per material
- On held-out materials and the independent UBO2014 benchmark, the decoder beats those trained on existing measured reflectance datasets and analytic PBR fitting, transferring to unseen acquisition setups without modification
- Dataset, calibration files, observation tensors and splits released under CC BY 4.0 on Hugging Face
Related event: RoboCloth: Massive Real-Cloth Dataset With 80 Billion Observations Released(4 posts)→
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