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.

本文探讨了基于物理规律的计算机视觉任务的制定和方法，提出了计算机视觉流水线的分类系统，并分别研究了如何在每个阶段中引入基本物理规律和控制方程，在计算机视觉领域中的应用前景是制定更好的计算机视觉模型以提高物理可行性、准确性、数据效率和泛化能力。