  We introduce the first end-to-end learning-based solution to near-field
Photometric Stereo (PS), where the light sources are close to the object of
interest. This setup is especially useful for reconstructing large immobile
objects. Our method is fast, producing a mesh from 52 512$\times$384 resolution
images in about 1 second on a commodity GPU, thus potentially unlocking several
AR/VR applications. Existing approaches rely on optimization coupled with a
far-field PS network operating on pixels or small patches. Using optimization
makes these approaches slow and memory intensive (requiring 17GB GPU and 27GB
of CPU memory) while using only pixels or patches makes them highly susceptible
to noise and calibration errors. To address these issues, we develop a
recursive multi-resolution scheme to estimate surface normal and depth maps of
the whole image at each step. The predicted depth map at each scale is then
used to estimate `per-pixel lighting' for the next scale. This design makes our
approach almost 45$\times$ faster and 2$^{\circ}$ more accurate (11.3$^{\circ}$
vs. 13.3$^{\circ}$ Mean Angular Error) than the state-of-the-art near-field PS
reconstruction technique, which uses iterative optimization.
