  A novel approach towards depth map super-resolution using multi-view
uncalibrated photometric stereo is presented. Practically, an LED light source
is attached to a commodity RGB-D sensor and is used to capture objects from
multiple viewpoints with unknown motion. This non-static camera-to-object setup
is described with a nonconvex variational approach such that no calibration on
lighting or camera motion is required due to the formulation of an end-to-end
joint optimization problem. Solving the proposed variational model results in
high resolution depth, reflectance and camera pose estimates, as we show on
challenging synthetic and real-world datasets.
