arXiv Computer Vision By Zhen Li (Simon Fraser University), Haoran Qin (Westlake University), Francesco Di Sario (University of Torino,Simon Fraser University), Daniel Rebain (University of British Columbia,Wayve), Andrea Tagliasacchi (Simon Fraser University, Wayve,University of Toronto)

RoboCloth: A Large-Scale Real Cloth Material Dataset for Neural Reflectance Reconstruction

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RoboCloth is a new dataset comprising 500 real cloth materials captured with robotically controlled cameras and lighting. Each material is imaged in about 580 high‑dynamic‑range frames across a wide view‑light hemisphere, with camera poses refined via structure‑from‑motion and aligned to the robot frame to provide per‑material sparse geometry and structured reflectance observations. The authors use this data to train a two‑stage neural reconstruction pipeline, demonstrating that a decoder trained on RoboCloth outperforms those trained on existing datasets and analytic PBR fitting, and transfers well to unseen acquisition systems.

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