Coloring the Past: Neural Historical Buildings Reconstruction from
  Archival Photography
David Komorowicz Lu Sang Ferdinand Maiwald Daniel Cremers
Historical buildings are a treasure and milestone of human cultural heritage.
Reconstructing the 3D models of these building hold significant value. The
rapid development of neural rendering methods makes it possible to recover the
3D shape only based on archival photographs. However, this task presents
considerable challenges due to the limitations of such datasets. Historical
photographs are often limited in number and the scenes in these photos might
have altered over time. The radiometric quality of these images is also often
sub-optimal. To address these challenges, we introduce an approach to
reconstruct the geometry of historical buildings, employing volumetric
rendering techniques. We leverage dense point clouds as a geometric prior and
introduce a color appearance embedding loss to recover the color of the
building given limited available color images. We aim for our work to spark
increased interest and focus on preserving historical buildings. Thus, we also
introduce a new historical dataset of the Hungarian National Theater, providing
a new benchmark for the reconstruction method.