Dense 3D Point Cloud Generation from Multiple High-resolution Spherical Images

Abstract

The generation of virtual models of cultural heritage assets is of high interest for documentation, restoration, development and promotion purposes. To this aim, non-invasive, easy and automatic techniques are required. We present a technology that automatically reconstructs large scale scenes from panoramic, high-resolution, spherical images. The advantage of the spherical panoramas is that they can acquire a complete environment in one single image. We show that the spherical geometry is more suited for the computation of the orientation of the panoramas (Structure from Motion) than the standard images, and introduce a generic error function for the epipolar geometry of spherical images. We then show how to produce a dense representation of the scene with up to 100 million points, that can serve as input for meshing and texturing software or for computer aided reconstruction. We demonstrate the applicability of our concept with reconstruction of complex scenes in the scope of cultural heritage documentation at the Chinese National Palace Museum of the Forbidden City in Beijing.