High-resolution facial geometry is essential for realistic digital avatars. Traditional reconstruction methods, such as multi-view stereo, often struggle with materials like skin, which exhibit complex light reflection, absorption, and scattering properties. Neural reconstruction methods have shown greater robustness to these view-dependent effects. However, positional-encoding-based implementations are typically slow, while faster hash-encoded methods
may falter under sparse camera views. We present a geometry reconstruction method tailored for an active-illumination facial capture setup featuring sparse cameras with varying characteristics. Our technique builds upon hash-encoded neural surface reconstruction, which we enhance with additional active-illumination-based
supervision and loss functions, allowing us to maintain high reconstruction speed and geometrical fidelity even with reduced camera coverage. We validate our approach through qualitative evaluations across diverse subjects, and quantitative evaluation using a synthetic dataset rendered with a virtual reproduction of our capture setup. Our results demonstrate that our method significantly out-performs previous neural reconstruction techniques on datasets
with sparse camera configurations.