  This paper introduces a versatile paradigm for integrating multi-view
reflectance (optional) and normal maps acquired through photometric stereo. Our
approach employs a pixel-wise joint re-parameterization of reflectance and
normal, considering them as a vector of radiances rendered under simulated,
varying illumination. This re-parameterization enables the seamless integration
of reflectance and normal maps as input data in neural volume rendering-based
3D reconstruction while preserving a single optimization objective. In
contrast, recent multi-view photometric stereo (MVPS) methods depend on
multiple, potentially conflicting objectives. Despite its apparent simplicity,
our proposed approach outperforms state-of-the-art approaches in MVPS
benchmarks across F-score, Chamfer distance, and mean angular error metrics.
Notably, it significantly improves the detailed 3D reconstruction of areas with
high curvature or low visibility.
