Abstract
Abstract 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
