Accepted Papers

Nostalgin: Extracting 3D City Models from Historical Image Data

Amol Kapoor, Hunter Larco and Raimondas Kiveris

What did it feel like to walk through a city from the past? In this work, we describe Nostalgin (Nostalgia Engine), a method that can faithfully reconstruct cities from historical images. Unlike existing work in city reconstruction, we focus on the task of reconstructing 3D cities from historical images. Working with historical image data is substantially more difficult, as there are significantly fewer buildings available and the details of the camera parameters which captured the images are unknown. Nostalgin can generate a city model even if there is only a single image per facade, regardless of viewpoint or occlusions. To achieve this, our novel architecture combines image segmentation, rectification, and inpainting. We motivate our design decisions with experimental analysis of individual components of our pipeline, and show that we can improve on baselines in both speed and visual realism. We demonstrate the efficacy of our pipeline by recreating two 1940s Manhattan city blocks. We aim to deploy Nostalgin as an open source platform where users can generate immersive historical experiences from their own photos.


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