  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.
