Recent advances in deep learning such as neural radiance fields and implicit
neural representations have significantly propelled the field of 3D
reconstruction. However, accurately reconstructing objects with complex optical
properties, such as metals and glass, remains a formidable challenge due to
their unique specular and light-transmission characteristics. To facilitate the
development of solutions to these challenges, we introduce the OpenMaterial
dataset, comprising 1001 objects made of 295 distinct materials-including
conductors, dielectrics, plastics, and their roughened variants- and captured
under 723 diverse lighting conditions. To this end, we utilized physics-based
rendering with laboratory-measured Indices of Refraction (IOR) and generated
high-fidelity multiview images that closely replicate real-world objects.
OpenMaterial provides comprehensive annotations, including 3D shape, material
type, camera pose, depth, and object mask. It stands as the first large-scale
dataset enabling quantitative evaluations of existing algorithms on objects
with diverse and challenging materials, thereby paving the way for the
development of 3D reconstruction algorithms capable of handling complex
material properties.

利用物理基础渲染和实验室测量的折射率，生成高保真度的多视角图像，我们引入 OpenMaterial 数据集，包括 295 种不同材料制成的 1001 个物体，并在 723 种不同的照明条件下采集。OpenMaterial 提供了全面的标注，包括 3D 形状、材料类型、相机姿态、深度和物体掩码。作为首个大规模数据集，它使得对具有多样且具有挑战性材料的物体上现有算法进行定量评估成为可能，从而为处理复杂材料属性的 3D 重建算法的发展铺平了道路。