Material Type Recognition of Indoor Scenes via Surface Reflectance Estimation

Authors
Lee, SeokyeongLee, DongjinKim, Hyun-CheolLee, Seungkyu
Issue Date
2022-01
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE Access, v.10, pp.134 - 143
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 appearance providing better description for material type recognition. In this work, we propose a material type recognition method based on both color and reflectance features using deep neural network. Proposed method is evaluated on both public and our own data sets showing promising material type recognition results.
Keywords
DISCRIMINATIVE ILLUMINATION; OPTIMAL PROJECTIONS; CLASSIFICATION; Cameras; Image color analysis; Estimation; Surface roughness; Rough surfaces; Three-dimensional displays; Surface reconstruction; Material type; surface reflectance
ISSN
2169-3536
URI
https://pubs.kist.re.kr/handle/201004/115830
DOI
10.1109/ACCESS.2021.3137585
Appears in Collections:
KIST Article > 2022
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