Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach
This work addresses depth enhancement for 3D sensing applications, presenting a novel method that eliminates calibration needs, though it is incremental in improving existing photometric stereo techniques.
The paper tackles the problem of depth map super-resolution by using a moving LED light source with an RGB-D sensor to capture objects from multiple viewpoints, and it achieves high-resolution depth, reflectance, and camera pose estimates without requiring calibration for lighting or motion.
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.