A Dictionary-based Approach for Estimating Shape and Spatially-Varying Reflectance
This work addresses a key challenge in computer vision for applications like 3D reconstruction and material analysis, though it appears incremental as it builds on existing photometric stereo techniques.
The paper tackles the problem of estimating object shape and spatially-varying reflectance from multiple images under varying illumination, achieving superior performance over competing methods on both simulated and real scenes.
We present a technique for estimating the shape and reflectance of an object in terms of its surface normals and spatially-varying BRDF. We assume that multiple images of the object are obtained under fixed view-point and varying illumination, i.e, the setting of photometric stereo. Assuming that the BRDF at each pixel lies in the non-negative span of a known BRDF dictionary, we derive a per-pixel surface normal and BRDF estimation framework that requires neither iterative optimization techniques nor careful initialization, both of which are endemic to most state-of-the-art techniques. We showcase the performance of our technique on a wide range of simulated and real scenes where we outperform competing methods.