Variational Reflectance Estimation from Multi-view Images

TL;DR

This paper introduces a variational method for estimating reflectance maps from multi-view images with known geometry, using a regularized approach that enforces consistency and smoothness, validated on synthetic and real data.

Contribution

It presents a novel variational framework that converts multi-view images into reflectance maps, simplifying computation by parameterizing reflectance in the image domain.

Findings

Effective reflectance estimation demonstrated on synthetic datasets.

Robustness of the method validated on real multi-view images.

Regularization improves reflectance map quality in under-constrained scenarios.

Abstract

We tackle the problem of reflectance estimation from a set of multi-view images, assuming known geometry. The approach we put forward turns the input images into reflectance maps, through a robust variational method. The variational model comprises an image-driven fidelity term and a term which enforces consistency of the reflectance estimates with respect to each view. If illumination is fixed across the views, then reflectance estimation remains under-constrained: a regularization term, which ensures piecewise-smoothness of the reflectance, is thus used. Reflectance is parameterized in the image domain, rather than on the surface, which makes the numerical solution much easier, by resorting to an alternating majorization-minimization approach. Experiments on both synthetic and real datasets are carried out to validate the proposed strategy.