Physics-Informed Computer Vision: A Review and Perspectives

Incorporation of physical information in machine learning frameworks are 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 formulation and approaches to computer vision tasks guided by physical laws, known as physics-informed computer vision. 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 each task are analyzed with regard to what governing physical processes are modeled for integration and how they are formulated to be incorporated, i.e. modify data (observation bias), modify networks (inductive bias), and modify losses (learning bias) to include physical rules. The taxonomy offers a unified view of the application of the physics-informed capability, highlighting where physics-informed machine learning has been conducted and where the gaps and opportunities are. Finally, we highlight open problems and challenges to inform future research avenues. 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.