A Variational Study on BRDF Reconstruction in a Structured Light Scanner

Abstract

Time-efficient acquisition of reflectance behavior together with surface geometry is a challenging problem. In this study, we investigate the impact of system parameter uncertainties when incorporating a data-driven BRDF reconstruction approach into the standard pipeline of a structured light scanning system. The parameters investigated include geometric detail of scanned objects; vertex positions and normals; and position and intensity of light sources. To have full control of uncertainties, experiments are carried out in a simulated environment, mimicking an actual structured light scanning setup. Results show that while uncertainties in vertex positions and normals have a high impact on the quality of reconstructed BRDFs, object geometry and light source properties have very little influence on the reconstructed BRDFs. With this analysis, practitioners now have insight in the tolerances required for accurate BRDF acquisition to work.

Material-Based Segmentation of Objects

“…Some of this work considers semantic segmentation [24], but the focus is labels related to an urban environment rather than materials. The appearance of materials depends on their reflectance properties, and rendering provides fine-tuned control over all parameters related to reflectance properties [21]. Image synthesis enables us to produce large scale, high precision, annotated datasets quickly.…”