Fully Automatic Mechanical Scan Range Extension and Signal to Noise Optimization of a Lens-Shifted Structured Light System : Eurographics Workshop on Graphics and Cultural Heritage (GCH)

Digitization of cultural heritage is of growing importance, both for its preservation for coming generations in the face of looming dangers of natural decay or intentional destruction, and current generations, that increasingly have access to virtual cultural heritage for interactive exploring or scientific analysis. These goals can only be achieved by 3D replicas at reasonable quality and resolution, to come as close as possible to the original. This brings about several challenges to overcome. The challenge of digitizing huge numbers of artefacts is addressed by CultLab3D, the first fully automatic 3D digitization system. Another challenge is the size of objects, as each digitization system is designed for a certain optimum measurement range, leaving which results in loss of quality. Due to optical and mechanical constraints, most systems are not able to faithfully reconstruct objects under a certain size limit in their full geometric detail. Historic coins are one good example, where the deterioration of the surface structure in most cases has progressed to a degree that it not even is perceptible through the fingernail. This challenge is addressed by a modular extension of CultLab3D, the MesoScanner, which is a structured light system that breaks limits in depth resolution through a mechanical lens-shifting extension, allowing physically shifting of fringe patterns on top of the well-known multi-period phase shift method. This is where this work adds two major improvements: First, the signal to noise ratio and thus reconstruction quality has been improved significantly through several algorithmic processing steps. Second, the physical limitation of the measurement range was removed using a 2D actuator steering the object mount, thus allowing for a measurement range at theoretically arbitrary size. This opens up the fully automatic handling of two scenarios: Complete digitization of objects exceeding the measurement range, and unsupervised digitization of large collections of small objects in one run.