3D surface Approximation of the Entire Bayeux Tapestry for Improved Pedagogical Access
Marjorie Redon, Matthieu Pizenberg, Yvain Quéau, Abderrahim Elmoataz · 2023
The Bayeux Tapestry is an exceptional cultural heritage masterpiece by its size and the finesse of its details. Digitizing it raises a challenge, knowing that it is extremely fragile and thus lasers or invasive techniques are out of scope. In this work, we address this 3D-reconstruction challenge by introducing a pipeline to generate a high-resolution panorama of the Tapestry’s geometry. It is based on a deep learning architecture that converts the RGB images of a pre-existing 2D panorama into a 2.5D normal map panorama. With a view to facilitating the Tapestry inclusive accessibility, we further show that coupling our 3D-reconstruction pipeline with a segmentation method allows the affordable and rapid creation of 3D-printed bas-reliefs, which can be explored tactilely by visually impaired people.