537573DMMA - 3D Capture with Measured Material AcquisitionClosedStudyInnovate UKComputer Graphics has progressed over the past 30 years from a scientific technique to being ubiquitous. It is a key part of Feature films, with ever increasing realism in battle scenes, aliens, and nearly realistic human like characters. Games are also in a similar state of being impressive, but 'not quite' real yet. Interesting demonstrations of Virtual Reality, involving the user wearing a headset, and Augmented Reality where the user sees a combination of the real world. augmented by CGI, are now possible. However this market, potentially worth half a trillion dollars in 2025 (Bloomberg prediction 13th February 2020) has stalled. Looking back at market predictions, success in always five years away. We have identified a key bottleneck, which is the creation of accurate and detailed 3D models that are needed across Film, Games, AR / VR experiences, and eCommerce. Currently the state of the art is to 'model' the item out of primitive shapes. This is very time consuming. The items created need to have not just the right 'shape' and contain the necessary detail, but the right 'appearance' - such as shiny steel, reflective marble, dull copper, fur, hessian, linen, and so on. Furthermore, this appearance needs to be convincing under many lighting conditions. A 3D artist can currently easily spend 1-2 weeks making even only a medium detailed model. Scanning techniques have been developed to try and increase the efficiency of making 3D models. However to date, no-one has managed to successfully demonstrate capture of both shape AND visual appearance in one pass. M-XR have developed a novel way of detecting and recording the visual properties of materials, based on a Machine Learning Approach. M-XR have built a crude prototype scanning system to indicate 'Proof of Concept'. Our project 3D Capture with Measured Material Acquisition (3DMMA) will develop a turnkey system for an automated scanning system that will record properly visual material properties. Initially this will be a 'service bureau scanning service', but later we will move into the sales of scanners to Media and Fashion companies. Beyond the end of our project described here, we believe that when a Machine Learning corpus of significant size exists, it will be possible to relax on the detailed object parameters that describe the object, and that one or more mobile phones could form the basis of a future system, being much cheaper and easier to handle.