{"title":"Interactions Between 3D Surface Shape and Material Perception.","authors":"Phillip J Marlow, Barton L Anderson","doi":"10.1146/annurev-vision-102122-094213","DOIUrl":null,"url":null,"abstract":"<p><p>Our visual systems are remarkably adept at deriving the shape and material properties of surfaces even when only one image of a surface is available. This ability implies that a single image of a surface contains potent information about both surface shape and material. However, from a computational perspective, the problem of deriving surface shape and material is formally ill posed. Any given image could be due to many combinations of shape, material, and illumination. Early computational models required prior knowledge about two of the three scene variables to derive the third. However, such models are biologically implausible because our visual systems are tasked with extracting all relevant scene variables from images simultaneously. This review describes recent progress in understanding how the visual system solves this problem by identifying complex forms of image structure that support its ability to simultaneously derive the shape and material properties of surfaces from images.</p>","PeriodicalId":48658,"journal":{"name":"Annual Review of Vision Science","volume":" ","pages":""},"PeriodicalIF":5.0000,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Review of Vision Science","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1146/annurev-vision-102122-094213","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Our visual systems are remarkably adept at deriving the shape and material properties of surfaces even when only one image of a surface is available. This ability implies that a single image of a surface contains potent information about both surface shape and material. However, from a computational perspective, the problem of deriving surface shape and material is formally ill posed. Any given image could be due to many combinations of shape, material, and illumination. Early computational models required prior knowledge about two of the three scene variables to derive the third. However, such models are biologically implausible because our visual systems are tasked with extracting all relevant scene variables from images simultaneously. This review describes recent progress in understanding how the visual system solves this problem by identifying complex forms of image structure that support its ability to simultaneously derive the shape and material properties of surfaces from images.
期刊介绍：
The Annual Review of Vision Science reviews progress in the visual sciences, a cross-cutting set of disciplines which intersect psychology, neuroscience, computer science, cell biology and genetics, and clinical medicine. The journal covers a broad range of topics and techniques, including optics, retina, central visual processing, visual perception, eye movements, visual development, vision models, computer vision, and the mechanisms of visual disease, dysfunction, and sight restoration. The study of vision is central to progress in many areas of science, and this new journal will explore and expose the connections that link it to biology, behavior, computation, engineering, and medicine.
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