Stereophotogrammetry is an emerging technique of scene understanding. Its
origins go back to at least the 1800s when people first started to investigate
using photographs to measure the physical properties of the world. Since then,
thousands of approaches have been explored. The classic geometric techniques of
Shape from Stereo is built on using geometry to define constraints on scene and
camera geometry and then solving the non-linear systems of equations. More
recent work has taken an entirely different approach, using end-to-end deep
learning without any attempt to explicitly model the geometry. In this survey,
we explore the overlap for geometric-based and deep learning-based frameworks.
We compare and contrast geometry enforcing constraints integrated into a deep
learning framework for depth estimation or other closely related problems. We
present a new taxonomy for prevalent geometry enforcing constraints used in
modern deep learning frameworks. We also present insightful observations and
potential future research directions.

立体光测法是一种新兴的场景理解技术，本文调查了基于几何和深度学习的框架之间的重叠点，并比较了几何约束与深度学习框架中用于深度估计等问题的集成；还提出了一个针对现代深度学习框架中流行几何约束的新分类法，并提出了一些深入观察和潜在的未来研究方向。