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A novel approach towards depth map super-resolution using multi-viewuncalibrated photometric stereo is presented. Practically, an LED light sourceis attached to a commodity RGB-D sensor and is used to capture objects frommultiple viewpoints with unknown motion. This non-static camera-to-object setupis described with a nonconvex variational approach such that no calibration onlighting or camera motion is required due to the formulation of an end-to-endjoint optimization problem. Solving the proposed variational model results inhigh resolution depth, reflectance and camera pose estimates, as we show onchallenging synthetic and real-world datasets.

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