  We introduce AutoRF - a new approach for learning neural 3D object
representations where each object in the training set is observed by only a
single view. This setting is in stark contrast to the majority of existing
works that leverage multiple views of the same object, employ explicit priors
during training, or require pixel-perfect annotations. To address this
challenging setting, we propose to learn a normalized, object-centric
representation whose embedding describes and disentangles shape, appearance,
and pose. Each encoding provides well-generalizable, compact information about
the object of interest, which is decoded in a single-shot into a new target
view, thus enabling novel view synthesis. We further improve the reconstruction
quality by optimizing shape and appearance codes at test time by fitting the
representation tightly to the input image. In a series of experiments, we show
that our method generalizes well to unseen objects, even across different
datasets of challenging real-world street scenes such as nuScenes, KITTI, and
Mapillary Metropolis.
