Wide field-of-view (FoV) cameras efficiently capture large portions of the
scene, which makes them attractive in multiple domains, such as automotive and
robotics. For such applications, estimating depth from multiple images is a
critical task, and therefore, a large amount of ground truth (GT) data is
available. Unfortunately, most of the GT data is for pinhole cameras, making it
impossible to properly train depth estimation models for large-FoV cameras. We
propose the first method to train a stereo depth estimation model on the widely
available pinhole data, and to generalize it to data captured with larger FoVs.
Our intuition is simple: We warp the training data to a canonical, large-FoV
representation and augment it to allow a single network to reason about diverse
types of distortions that otherwise would prevent generalization. We show
strong generalization ability of our approach on both indoor and outdoor
datasets, which was not possible with previous methods.

我们提出了一种在大视场摄像机数据上训练立体深度估计模型的方法，并能够推广到具有更大视场的数据。我们通过将训练数据变换为规范的大视场表示，并进行扩充，使单个网络能够推理出各种类型的扭曲，从而展示了我们方法在室内外数据集上的强大泛化能力，而这在以前的方法中是不可能的。