  Neural approaches have shown a significant progress on camera-based
reconstruction. But they require either a fairly dense sampling of the viewing
sphere, or pre-training on an existing dataset, thereby limiting their
generalizability. In contrast, photometric stereo (PS) approaches have shown
great potential for achieving high-quality reconstruction under sparse
viewpoints. Yet, they are impractical because they typically require tedious
laboratory conditions, are restricted to dark rooms, and often multi-staged,
making them subject to accumulated errors. To address these shortcomings, we
propose an end-to-end uncalibrated multi-view PS framework for reconstructing
high-resolution shapes acquired from sparse viewpoints in a real-world
environment. We relax the dark room assumption, and allow a combination of
static ambient lighting and dynamic near LED lighting, thereby enabling easy
data capture outside the lab. Experimental validation confirms that it
outperforms existing baseline approaches in the regime of sparse viewpoints by
a large margin. This allows to bring high-accuracy 3D reconstruction from the
dark room to the real world, while maintaining a reasonable data capture
complexity.
