Geometric Constraints in Deep Learning Frameworks: A Survey
Vibhas K Vats David J Crandall
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.