Using noise function-based patterns to enhance photogrammetric 3D reconstruction performance of featureless surfaces

One of the factors that determine the data quality produced by targetless photogrammetric techniques is the feature richness of the surface being captured. The Structure-From-Motion and Multiple View Stereovision (SFM-MVS) pipeline is no exception to this rule as it relies on the ability to identify corresponding points within a collection of unordered images. In this work, we question the introduction of noise function-based pattern (NFP) projection in the SFM-MVS data collection phase in order to enhance the reconstruction performance when applied on featureless surfaces. We selected a set of NFPs and we demonstrate their reconstruction performance enhancement on a Cycladic figurine by using a commercial SFM-MVS software package. We quantify each NFP's behaviour in relation to the produced data. We correlate the reconstruction results with band limiting and aliasing pattern characteristics. We compare the SFM-MVS data with those produced by digitising the same artefact with a laser triangulation scanner. We discuss the NFPs performance along with the advantages of the proposed methodology and its limitations.