Modern 3D computer vision leverages learning to boost geometric reasoning,
mapping image data to classical structures such as cost volumes or epipolar
constraints to improve matching. These architectures are specialized according
to the particular problem, and thus require significant task-specific tuning,
often leading to poor domain generalization performance. Recently, generalist
Transformer architectures have achieved impressive results in tasks such as
optical flow and depth estimation by encoding geometric priors as inputs rather
than as enforced constraints. In this paper, we extend this idea and propose to
learn an implicit, multi-view consistent scene representation, introducing a
series of 3D data augmentation techniques as a geometric inductive prior to
increase view diversity. We also show that introducing view synthesis as an
auxiliary task further improves depth estimation. Our Depth Field Networks
(DeFiNe) achieve state-of-the-art results in stereo and video depth estimation
without explicit geometric constraints, and improve on zero-shot domain
generalization by a wide margin.

提出了一种使用 Transformer 架构学习隐式多视图一致场景表示并引入一系列 3D 数据增强技术的方法来增加视角多样性的深度估计网络，同时介绍了引入视角合成作为辅助任务，该网络在不使用显式几何约束的情况下在立体和视频深度估计方面取得了最先进的结果， 并在零样本领域泛化方面有了显著的提高。