Modelling of material appearance from reflectance measurements has become increasingly prevalent due to the development of novel methodologies in Computer Graphics. In the last few years, some advances have been made in measuring the light-material interactions, by employing goniometers/reflectometers under specific laboratory’s constraints. A wide range of applications benefit from data-driven appearance modelling techniques and material databases to create photorealistic scenarios and physically based simulations. However, important limitations arise from the current material scanning process, mostly related to the high diversity of existing materials in the real-world, the tedious process for material scanning and the spectral characterisation behaviour. Consequently, new approaches are required both for the automatic material acquisition process and for the generation of measured material databases. In this study, a novel approach for material appearance acquisition using hyperspectral data is proposed. A dense 3D point cloud filled with spectral data was generated from the images obtained by an unmanned aerial vehicle (UAV) equipped with an RGB camera and a hyperspectral sensor. The observed hyperspectral signatures were used to recognise natural and artificial materials in the 3D point cloud according to spectral similarity. Then, a parametrization of Bidirectional Reflectance Distribution Function (BRDF) was carried out by sampling the BRDF space for each material. Consequently, each material is characterised by multiple samples with different incoming and outgoing angles. Finally, an analysis of BRDF sample completeness is performed considering four sunlight positions and 16x16 resolution for each material. The results demonstrated the capability of the used technology and the effectiveness of our method to be used in applications such as spectral rendering and real-word material acquisition and classification.

由于计算机图形学中新方法的发展，基于反射率测量的材料外观建模变得越来越普遍。在过去几年中，通过在特定实验室的限制下使用测角仪/反射计，在测量光-材料相互作用方面取得了一些进展。广泛的应用程序受益于数据驱动的外观建模技术和材料数据库，以创建逼真的场景和基于物理的模拟。然而，当前的材料扫描过程产生了重要的局限性，主要与现实世界中现有材料的高度多样性、材料扫描的繁琐过程和光谱表征行为有关。最后，自动材料采集过程和测量材料数据库的生成都需要新的方法。在这项研究中，提出了一种使用高光谱数据获取材料外观的新方法。从配备 RGB 相机和高光谱传感器的无人驾驶飞行器 (UAV) 获得的图像生成充满光谱数据的密集 3D 点云。观察到的高光谱特征用于根据光谱相似性识别 3D 点云中的天然和人造材料。然后，通过对每种材料的 BRDF 空间进行采样来执行双向反射分布函数 (BRDF) 的参数化。因此，每种材料的特征是具有不同入射角和出射角的多个样品。最后，考虑到每种材料的四个阳光位置和 16x16 分辨率，对 BRDF 样品完整性进行了分析。结果证明了所用技术的能力以及我们的方法在光谱渲染和实词材料采集和分类等应用中的有效性。