DigiArt project has demonstrated the progress beyond the state-of-the-art in some research areas. These are briefly outlined below:
Comparative UAV and Ground-based 3D Scanning.
Ground-based scanning, achieved by mounting cameras in an array of positions mounted on tripods and taking a series of images, is a well-established technology. As such it acts as baseline standard. Airborne 3D scanning using a derivative of photogrammetry.
New Flight Path Design methodologies to Optimise SfM.
The airborne scan of the cave using UAVs yielded some excellent data. The resultant 3D model is of very high quality. But the processing time required to produce it was excessive. Investigations were carried out to determine why the processing was taking so much time. This revealed that the principal problem was not the large number of mathematical operations required, but the constant swapping in and out of memory of images in the sequence separated by considerable temporal distance and only small physical distances.
GPS Denied Enclosed Space 3D Scanning.
We have shown via our UAV scan of the Scladina cave, that it is possible to gather comprehensive 3D data on the boundaries of a completely enclosed space which is GPS denied.
The Desktop Open Source Scanner.
The realisation of the new desktop, static, scanner offers the prospect of ultra-low-cost 3D scanning capability for every museum in Europe.
Using Deep Learning Architectures for 3D Data Analysis.
Recent advancements in 3D sensing technology and the appearance of low-cost devices such as Microsoft Kinect have made the collection of 3D data more feasible and affordable than ever. Based on the scanning device employed for capturing the 3D scene or object of interest, raw data are collected in different forms. UAV scanners get range images from different camera viewpoints. Then, these images are typically combined through a registration process or Structure-from-Motion techniques (SfM) in order to discard noisy data, establish correspondences between them and ultimately generate a unified 3D point cloud for further processing. The increasing abundance of 3D data encouraged the research community to exploit this richer content for addressing several computer vision problems related to understanding 3D scenes, e.g. 3D Object Classification, 3D Object Recognition and 3D Shape Retrieval.
Story Telling Engine in a Flexible Architecture.
In designing the architecture of our story-telling engine, we had to address a number of requirements including expandability, generalizability, user-friendliness, immersiveness, compatibility to several platforms, and the ability for the final product to be commercialised. In the past several attempts to make VR/AR tours were discontinued due to the lack of an appropriate architecture that will be self-sustained and commercially active. The architecture that has been proposed as part of WP6 activities focuses on reducing, as much as possible, the interference of a programmer or a game designer so that the main target group, i.e. the curators, can easily make their own VR/AR tours and maintain it. This was achieved by proposing an architecture that interconnects a web-based interface for 3D content management with the powerful game making engine of Unity. In this way, we were able to abstract the process of setting the 3D scene and defining triggers on the 3D objects, without sacrificing the quality of the resulting game.
