Probabilistic Phase Unwrapping for Time-of-Flight Cameras

Abstract:
Time-of-Flight (ToF) cameras gain depth information by emitting amplitude-modulated near-infrared light and measuring the phase shift between the emitted and the reflected signal. The phase shift is proportional to the object’s distance modulo the wavelength of the modulation frequency. This results in a distance ambiguity. Distances larger than the wavelength are wrapped into the sensor’s non-ambiguity range and cause spurious distance measurements. We apply Phase Unwrapping to reconstruct these wrapped measurements. Our approach is based on a probabilistic graphical model using loopy belief propagation to detect and infer the position of wrapped measurements. In experiments, we show that wrapped measurements are identified and corrected allowing to reconstruct the structure of the scene.