Near-light Photometric Stereo with Symmetric Lights
This work addresses shape recovery in computer vision by providing a more efficient approach for near-light photometric stereo, though it appears incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of near-light photometric stereo by introducing a linear solution method that uses symmetric light source arrangements, achieving comparable accuracy to state-of-the-art calibrated methods while reducing the need for initialization and calibration.
This paper describes a linear solution method for near-light photometric stereo by exploiting symmetric light source arrangements. Unlike conventional non-convex optimization approaches, by arranging multiple sets of symmetric nearby light source pairs, our method derives a closed-form solution for surface normal and depth without requiring initialization. In addition, our method works as long as the light sources are symmetrically distributed about an arbitrary point even when the entire spatial offset is uncalibrated. Experiments showcase the accuracy of shape recovery accuracy of our method, achieving comparable results to the state-of-the-art calibrated near-light photometric stereo method while significantly reducing requirements of careful depth initialization and light calibration.