Neural rerendering in the wild
CVPR 2019 (oral).
Learn views under varying appearance from internet photos and reconstructed points.
We explore total scene capture — recording, modeling, and rerendering a scene under varying
appearance such as season and time of day. Starting from internet photos of a tourist landmark, we apply
traditional 3D reconstruction to register the photos and approximate the scene as a point cloud. For each
photo, we render the scene points into a deep framebuffer, and train a neural network to learn the mapping
of these initial renderings to the actual photos. This rerendering network also takes as input a latent
appearance vector and a semantic mask indicating the location of transient objects like pedestrians. The
model is evaluated on several datasets of publicly available images spanning a broad range of illumination
conditions. We create short videos demonstrating realistic manipulation of the image viewpoint, appearance,
and semantic labeling. We also compare results with prior work on scene reconstruction from internet
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