Deep learning techniques have enabled rapid progress in monocular depth
estimation, but their quality is limited by the ill-posed nature of the problem
and the scarcity of high quality datasets. We estimate depth from a single
camera by leveraging the dual-pixel auto-focus hardware that is increasingly
common on modern camera sensors. Classic stereo algorithms and prior
learning-based depth estimation techniques under-perform when applied on this
dual-pixel data, the former due to too-strong assumptions about RGB image
matching, and the latter due to not leveraging the understanding of optics of
dual-pixel image formation. To allow learning based methods to work well on
dual-pixel imagery, we identify an inherent ambiguity in the depth estimated
from dual-pixel cues, and develop an approach to estimate depth up to this
ambiguity. Using our approach, existing monocular depth estimation techniques
can be effectively applied to dual-pixel data, and much smaller models can be
constructed that still infer high quality depth. To demonstrate this, we
capture a large dataset of in-the-wild 5-viewpoint RGB images paired with
corresponding dual-pixel data, and show how view supervision with this data can
be used to learn depth up to the unknown ambiguities. On our new task, our
model is 30% more accurate than any prior work on learning-based monocular or
stereoscopic depth estimation.

通过使用现代摄像头上越来越常见的双像素自动对焦硬件，识别深度估计中的不确定性并开发了一种方法解决它，使基于学习的方法可以很好地应用于双像素图像，提高了成像质量。