Three-dimensional point cloud alignment detecting fiducial markers by structured light stereo imaging
In recent years, various methodologies of shape reconstruction have been proposed with the aim at creating Computer-Aided Design models by digitising physical objects using optical sensors. Generally, the acquisition of 3D geometrical data includes crucial tasks, such as planning scanning strategies and aligning different point clouds by multiple view approaches, which differ for user's interaction and hardware cost. This paper describes a methodology to automatically measure three-dimensional coordinates of fiducial markers to be used as references to align point clouds obtained by an active stereo vision system based on structured light projection. Intensity-based algorithms and stereo vision principles are combined to detect passive fiducial markers localised in a scene. 3D markers are uniquely recognised on the basis of geometrical similarities. The correlation between fiducial markers and point clouds allows the digital creation of complete object surfaces. The technology has been validated by experimental tests based on nominal benchmarks and reconstructions of target objects with complex shapes.
stereo vision reverse engineering digital image processing multiple view scanning marker detection