Reflectometry is the task for acquiring the bidirectional reflectance distribution function (BRDFs) of real-world materials. The typical reflectometry pipeline in computer vision, computer graphics, and computational imaging involves capturing images of a convex shape under multiple illumination and imaging conditions; due to the convexity of the shape, which implies that all paths from the light source to the camera perform a single reflection, the intensities in these images can subsequently be analytically mapped to BRDF values. We deviate from this pipeline by investigating the utility of higher-order light transport effects, such as the interreflections arising when illuminating and imaging a concave object, for reflectometry. We show that interreflections provide a rich set of contraints on the unknown BRDF, significantly exceeding those available in equivalent measurements of convex shapes. We develop a differentiable rendering pipeline to solve an inverse rendering problem that uses these constraints to produce high-fidelity BRDF estimates from even a single input image. Finally, we take first steps towards designing new concave shapes that maximize the amount of information about the unknown BRDF available in image measurements. We perform extensive simulations to validate the utility of this reflectometry from interreflections approach.
Our implementation is available at the following GitHub repository, and includes a differentiable renderer that can optimize for reflectance properties. It uses Mitsuba for efficient rendering, and TensorFlow for stochastic gradient descent optimization.
For an in-depth description of this work, please refer to our paper, supplementary material, and ICCP presentation video.
Kfir Shem-Tov, Sai Praveen Bangaru, Anat Levin, and Ioannis Gkioulekas. "Towards Reflectometry from Interreflections", ICCP 2020
This work was supported by ERC 635537, ISF 1046-14, Ollendorff Minerva Center of the Technion, NSF Expeditions award 1730147, NSF award IIS-1900849, and a gift from the AWS Cloud Credits for Research program.