  We present SuperNormal, a fast, high-fidelity approach to multi-view 3D
reconstruction using surface normal maps. With a few minutes, SuperNormal
produces detailed surfaces on par with 3D scanners. We harness volume rendering
to optimize a neural signed distance function (SDF) powered by multi-resolution
hash encoding. To accelerate training, we propose directional finite difference
and patch-based ray marching to approximate the SDF gradients numerically.
While not compromising reconstruction quality, this strategy is nearly twice as
efficient as analytical gradients and about three times faster than
axis-aligned finite difference. Experiments on the benchmark dataset
demonstrate the superiority of SuperNormal in efficiency and accuracy compared
to existing multi-view photometric stereo methods. On our captured objects,
SuperNormal produces more fine-grained geometry than recent neural 3D
reconstruction methods.
