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.

AutoRF 是一种学习神经 3D 物体表示的新方法，它通过单个视角观察训练集中的每个对象，提出了一种标准化的、以对象为中心的表示方法，该方法描述并区分了形状、外观和姿态，并在单个输出中解码为新的目标视图，从而实现了新颖的视图合成，进一步通过优化形状和外观的编码，在测试时将表示紧密拟合到输入图像，实验表明该方法能够很好地适应不同数据集和挑战性现实场景下的未见过的对象。