RoboCloth：用于神经反射率重建的大规模真实布料材质数据集
RoboCloth: A Large-Scale Real Cloth Material Dataset for Neural Reflectance Reconstruction
AI总结：
该研究提出RoboCloth大规模真实布料材质数据集，构建两阶段神经重建管道，其训练的解码器在材质重建任务上优于现有方法，可迁移至新采集系统，相关资源已开源。
摘要：
我们提出了RoboCloth，这是一个包含500种真实布料材质的数据集，这些材质是在灵活、经过校准的相机和光照配置下采集的，采集过程中相机和光照均由机器人控制。每种材质在覆盖宽视角-光照半球的约580个高动态范围帧中被成像；相机位姿通过运动恢复结构（SfM）进行优化，并与机器人坐标系对齐，从而得到每种材质的稀疏表面几何结构和结构化的逐点反射率观测数据，可直接用于神经材质训练。我们通过一个两阶段神经重建管道展示了该数据的价值：第一阶段在我们的数据集上训练一个共享的MLP解码器，以学习布料材质先验；第二阶段冻结该解码器，并为每个目标材质拟合一个密集的2048×2048潜在纹理。在我们自己数据集的保留材质以及独立的UBO2014基准上进行评估，结果表明，在我们的采集数据上训练的解码器优于在现有测量反射率数据集和解析PBR拟合上训练的解码器，且无需修改即可迁移到未见过的采集系统。该数据集、校准文件、观测张量以及训练/测试划分已在Hugging Face Hub上以CC BY 4.0协议发布。
英文摘要：
We present RoboCloth, a dataset of 500 real cloth materials captured under flexible, calibrated camera and lighting configurations, where both are robotically controlled during acquisition. Each material is imaged in approximately 580 high-dynamic range frames spanning a wide view-light hemisphere; camera poses are refined via structure-from-motion and aligned to the robot frame, yielding per-material sparse surface geometry and structured per-point reflectance observations ready for neural material training. We demonstrate the value of this data through a two-stage neural reconstruction pipeline: Stage 1 trains a shared MLP decoder on our corpus to learn cloth material priors; Stage 2 then freezes the decoder and fits a dense 2048x2048 latent texture to each target material. Evaluating on held-out materials from our own dataset and on the independent UBO2014 benchmark, we show that a decoder trained on our captures outperforms decoders trained on existing measured reflectance datasets and analytic PBR fitting, transferring to unseen acquisition systems without modification. The dataset, calibration files, observation tensors, and train/test splits are released under CC BY 4.0 on the Hugging Face Hub.
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