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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 are analyzed in terms of modeling and formulation of governing physical processes, including modifying input data (observation bias), network architectures (inductive bias), and 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.<\/jats:p>","DOI":"10.1145\/3689037","type":"journal-article","created":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T11:22:26Z","timestamp":1724152946000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":53,"title":["Physics-Informed Computer Vision: A Review and Perspectives"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1039-3744","authenticated-orcid":false,"given":"Chayan","family":"Banerjee","sequence":"first","affiliation":[{"name":"School of Electrical Engneering and Robotics, Queensland University of Technology, Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3466-9218","authenticated-orcid":false,"given":"Kien","family":"Nguyen","sequence":"additional","affiliation":[{"name":"School of Electrical Engneering and Robotics, Queensland University of Technology, Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8515-6324","authenticated-orcid":false,"given":"Clinton","family":"Fookes","sequence":"additional","affiliation":[{"name":"School of Electrical Engneering and Robotics, Queensland University of Technology, Brisbane, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9713-7120","authenticated-orcid":false,"given":"Karniadakis","family":"George","sequence":"additional","affiliation":[{"name":"School of Engineering, Brown University, Providence, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,7]]},"reference":[{"issue":"2","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1049\/ipr2.12359","article-title":"Real-world super-resolution of face-images from surveillance cameras","volume":"16","author":"Aakerberg Andreas","year":"2022","unstructured":"Andreas Aakerberg, Kamal Nasrollahi, and Thomas B. 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