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Journal Article
Material Type Recognition of Indoor Scenes via Surface Reflectance Estimation
- Authors
- Seokyeong Lee, Dongjin Lee, Hyun-Cheol Kim, Seungkyu Lee
- Issue Date
- 2022-01
- Citation
- IEEE Access, v.10, pp.134-143
- ISSN
- 2169-3536
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- 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.
- KSP Keywords
- Color camera, Data sets, Deep neural network(DNN), Depth sensor, Indoor scenes, Recognition method, Reflectance estimation, Visual appearance, Visual features, material type(Cellular), surface reflectance
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
