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The highest quality 3D face reconstructions are produced using multi-view stereo methods, reporting errors below 0.5mm. Unfortunately, these methods typically employ dozens of high-resolution cameras in a large laboratory capture gantry. In contrast, monocular 3D face reconstruction using sophisticated deep learning models are suited for casual mobile phone imaging outside the lab and report a mean error of 1-2mm.This paper investigates whether classic stereo methods can be used in scenarios with only a few low-resolution images available. We expect to find that it cannot since multiview stereo performs well only when many high-resolution images are provided. When only two low-resolution images are available, stereo produces very noisy results which are not directly usable. Surprisingly, however, our analysis shows that this visually noisy data has lower error than comparison state-of-the-art methods. We find that the visual artifacts from stereo can be removed using a morphable face model to constrain face shape.

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