상세정보
학술지
Material Type Recognition of Indoor Scenes via Surface Reflectance Estimation
- 저자
- 이석영, 이동진, 김현철, 이승규
- 발행일
- 202201
- 출처
- IEEE Access, v.10, pp.134-143
- ISSN
- 2169-3536
- 출판사
- IEEE
- 협약과제
- 21ZH1200, 초실감 입체공간 미디어·콘텐츠 원천기술연구, 이태진
- 초록
- 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 제안 키워드
- 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
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