An RGBN Benchmark

A variety of algorithms in both computer vision and graphics use datasets of an object or scene captured with fixed camera but varying illumination. Evaluating these algorithms is frequently challenging because of the lack of ground truth on the one hand, and insufficiently realistic and varied synthetic datasets on the other. In this work, we present a synthetic benchmark for applications such as photometric stereo, and justify it by comparing to real-life objects and their rendered models. Additionally, we introduce a system that allows the user to create scenes by combining arbitrary 3D models, materials, and light configurations. The system outputs physically-based renderings as well as dense ground-truth maps of quantities such as normals, height map, BRDF specifications, and albedo. We present a number of synthetic datasets which will be available online, and we provide a few photometric datasets of real-life objects. Our work demonstrates that real objects can be simulated well enough so that the conclusions about accuracy drawn from our synthetic datasets match those based on real objects. The paper also demonstrates a use case for this RGBN benchmark: the evaluation of photometric stereo algorithms. We present a taxonomy of photometric stereo techniques, investigate the causes of errors in several of them, and propose a photometric stereo variant that iteratively estimates shadowing.