SeokYeong Lee Hwasup Lim Ahn, Sang Chul SeungKyu Lee 2024-01-12T04:42:53Z 2024-01-12T04:42:53Z 2021-09-29 2019-07 - https://pubs.kist.re.kr/handle/201004/78511 Recently, material type recognition using color or light field camera has been studied. However, visual pattern based approaches for material type recognition without direct acquisition of surface reflectance show limited performance. In this work, we propose IR surface reflectance estimation using off-the-shelf ToF (Time-of-Flight) active sensor such as Kinect and perform surface material type recognition based on both color and reflectance clues. Two stream deep neural network consists of convolutional neural network encoding visual clue and recurrent neural network encoding reflectance characteristic is proposed for material classification. Estimated IR surface reflectance and material type recognition evaluation on our Color-IR Material Data set show promising performance compared to prior approaches. English SIGGRAPH IR Surface Reflectance Estimation and Material Type Recognition using Two-stream Net and Kinect Camera Conference 10.1145/3306214.3338557 1 SIGGRAPH '19: Special Interest Group on Computer Graphics and Interactive Techniques Conference SIGGRAPH '19: Special Interest Group on Computer Graphics and Interactive Techniques Conference US Los Angeles California 2019-07-28 SIGGRAPH '19: Special Interest Group on Computer Graphics and Interactive Techniques Conference