2022
DOI: 10.1109/access.2021.3137585
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Material Type Recognition of Indoor Scenes via Surface Reflectance Estimation

Abstract: There are fundamental difficulties in obtaining material type of an arbitrary object using traditional sensors. Existing material type recognition methods mostly focus on color based visual features and object-prior. Surface reflectance is another critical clue in the characterization of certain material type and can be observed by traditional sensors such as color camera and time-of-flight depth sensor. A material type is characterized well by relevant surface reflectance together with traditional visual appe… Show more

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Cited by 2 publications
(2 citation statements)
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“…They tested the simplicity, effectiveness, robustness, and efficiency of the HMF2 model on two benchmark datasets. Lee et al [27] proposed a material-type identification method using a deep CNN based on color and reflectance features. The proposed method was evaluated on public datasets, showing promising results for material type identification.…”
Section: Literature Reviewmentioning
confidence: 99%
“…They tested the simplicity, effectiveness, robustness, and efficiency of the HMF2 model on two benchmark datasets. Lee et al [27] proposed a material-type identification method using a deep CNN based on color and reflectance features. The proposed method was evaluated on public datasets, showing promising results for material type identification.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Several authors have looked at using off-the-shelf time-of-flight (ToF) cameras to exploit depth errors for classifying materials in an image, independent of the material color [7,11]; Tanaka et al also demonstrated that the accuracy could be greatly improved from 55.0% to 89.9% by sweeping the modulation frequency as well [7].…”
Section: Introductionmentioning
confidence: 99%