3D shape measurement of static and moving objects with adaptive spatiotemporal correlation

Common correlation-based photogrammetric 3D shape measurement techniques evaluate either temporal or spatial features. Temporal approaches achieve high accuracies but are limited to the measurement of static objects. Spatial techniques can deal with moving objects but provide relatively inaccurate results. Our goal is to combine these methods in order to measure dynamic scenes that contain static and moving objects. Therefore, we present a spatiotemporal correlation that adapts its temporal and spatial support locally to the motion of the measured objects. In addition, our technique compensates motion by warping the correlated image regions temporally. Our approach is based on structured illumination of random patterns, which are well suited for dynamic scenes due to high possible frame rates. The proposed technique is tested with simulated data and real measurements. (C) 2014 Optical Society of America