Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach

Lu Sang, Bjoern Haefner, Daniel Cremers

Abstract:

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 nonstatic 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.