Coloring the Past: Neural Historical Monuments Reconstruction from Archival Photography

Dávid Komorowicz, Lu Sang, Ferdinand Maiwald, Daniel Cremers

Keywords: Neural rendering, Historical monuments, 3D reconstruction

TL;DR: Reconstructing lost Historic monuments in color from poor quality and sparse photos + new dataset

Abstract: Historical monuments are a treasure and milestone of cultural heritage. Reconstructing the 3D models of these buildings holds significant value. The rapid development of neural rendering methods makes it possible to recover the original 3D shape exclusively based on archival photographs. However, this task presents considerable challenges due to the properties of available color images. Historical pictures are often limited in number and the scenes in these photos might have altered over time. The radiometric quality of these images is often sub-optimal for using automatic methods. To address these challenges, we introduce an approach to reconstruct the geometry of historical buildings from limited input images. We leverage dense point clouds as a geometric prior and introduce a color appearance embedding loss in volumetric rendering to recover the color of the building. We aim for our work to spark increased interest and focus on preserving historic buildings. Together with the proposed method, we introduce a new historical dataset of the Hungarian National Theater, providing a new benchmark for 3D reconstruction.

Submission Number: 37