  The incorporation of physical information in machine learning frameworks is
opening and transforming many application domains. Here the learning process is
augmented through the induction of fundamental knowledge and governing physical
laws. In this work, we explore their utility for computer vision tasks in
interpreting and understanding visual data. We present a systematic literature
review of more than 250 papers on formulation and approaches to computer vision
tasks guided by physical laws. We begin by decomposing the popular computer
vision pipeline into a taxonomy of stages and investigate approaches to
incorporate governing physical equations in each stage. Existing approaches in
computer vision tasks are analyzed with regard to what governing physical
processes are modeled and formulated, and how they are incorporated, i.e.
modification of input data (observation bias), modification of network
architectures (inductive bias), and modification of training losses (learning
bias). The taxonomy offers a unified view of the application of the
physics-informed capability, highlighting where physics-informed learning has
been conducted and where the gaps and opportunities are. Finally, we highlight
open problems and challenges to inform future research. While still in its
early days, the study of physics-informed computer vision has the promise to
develop better computer vision models that can improve physical plausibility,
accuracy, data efficiency, and generalization in increasingly realistic
applications.
