  We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering
Benchmark. Recent advances in inverse rendering have enabled a wide range of
real-world applications in 3D content generation, moving rapidly from research
and commercial use cases to consumer devices. While the results continue to
improve, there is no real-world benchmark that can quantitatively assess and
compare the performance of various inverse rendering methods. Existing
real-world datasets typically only consist of the shape and multi-view images
of objects, which are not sufficient for evaluating the quality of material
recovery and object relighting. Methods capable of recovering material and
lighting often resort to synthetic data for quantitative evaluation, which on
the other hand does not guarantee generalization to complex real-world
environments. We introduce a new dataset of real-world objects captured under a
variety of natural scenes with ground-truth 3D scans, multi-view images, and
environment lighting. Using this dataset, we establish the first comprehensive
real-world evaluation benchmark for object inverse rendering tasks from
in-the-wild scenes, and compare the performance of various existing methods.
