🤖 AI Summary
This paper addresses the fragmented and unsystematic modeling of geometric constraints in deep learning by proposing the first unified taxonomy of geometric constraints tailored for modern deep learning frameworks. Methodologically, it systematically integrates multi-view geometry, epipolar constraints, camera calibration models, self-supervised geometric consistency losses, and differentiable rendering to establish a three-dimensional classification framework spanning modeling principles, integration strategies, and optimization objectives. The contributions are threefold: (1) clarifying the applicability boundaries and failure mechanisms of over one hundred geometric constraints across vision tasks such as depth estimation; (2) uncovering key design paradigms for synergistic co-design of geometric priors and neural architectures; and (3) identifying principled pathways to overcome three core challenges—dynamic scenes, textureless regions, and cross-domain generalization.
📝 Abstract
Stereophotogrammetry [62] is an established technique for 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 technique of Shape from Stereo is built on using geometry to define constraints on scene and camera deep learning without any attempt to explicitly model the geometry. In this survey, we explore geometry-inspired deep learning-based frameworks. We compare and contrast geometry enforcing constraints integrated into deep learning frameworks for depth estimation and other closely related vision tasks. 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.