Geometric Constraints in Deep Learning Frameworks: A Survey

Abstract

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.

Tasks

Reproductions