ArtLab is specialised in complex high-resolution 3D documentation of cultural heritage. We work across a wide range of scales, from tiny highly detailed objects to monumental artworks, architectural interiors and historic environments. Where standard approaches are insufficient, we develop tailor-made acquisition workflows to address challenges of scale, accessibility, surface properties or material complexity. Our methods include photogrammetry, LiDAR, microphotogrammetry and multispectral 3D imaging.

ArtLab develops high-resolution 2.5D imaging workflows for the study of shallow relief and fine surface topography. Techniques such as Photometric Stereo and Reflectance Transformation Imaging (RTI) allow subtle variations in texture, tooling, incisions, printing, wear and surface deformation to be visualised and analysed in ways that are difficult to achieve with conventional photography. ArtLab also develops custom experimental hardware and software solutions for automated acquisition, surface reconstruction, relighting and analysis.

Multispectral imaging can reveal features of artworks and heritage objects that are difficult or impossible to observe under normal illumination. ArtLab works with high-resolution photography, ultraviolet fluorescence photography (UVF), infrared photography (IR) and reflectography (IRR) and related imaging techniques. We combine these methods where appropriate into registered multimodal datasets, allowing different forms of information to be studied and compared directly.

ArtLab develops integrated research workflows in which imaging techniques are combined with analytical methods for the identification and characterisation of materials. Depending on the research question, these may include X-ray fluorescence (XRF and MA-XRF), spectroscopy and other methods carried out in collaboration with specialised research partners. By bringing imaging and analytical data together, we can investigate materials, production processes, alterations and conservation histories across different spatial scales.

Experimental research with materials and their making to shed new light on historical as well as contemporary material practices and expertise across the arts, crafts and sciences.

ArtLab applies image processing, computer vision, machine learning and quantitative methods to both individual objects and larger collections. Computational approaches can help identify, measure and compare visual or material features that are difficult to assess manually. Applications range from pattern, surface and watermark analysis to image registration, dataset comparison, network theory approaches, automated feature detection and the quantitative analysis of larger groups of artworks.

ArtLab develops interactive ways of exploring, interpreting and presenting research data. These range from high-resolution image viewers and interactive 3D models to 3D animations and virtual reality experiences. Rather than treating visualisation as a separate final step, we explore how digital environments can function both as research tools and as ways of making complex cultural heritage data accessible to wider audiences. The international quality and impact of this work have been recognised through several industry awards (DIA, Lovie).