  We propose the method that uses only computer graphics datasets to parse the
real world 3D scenes. 3D scene parsing based on semantic segmentation is
required to implement the categorical interaction in the virtual world.
Convolutional Neural Networks (CNNs) have recently shown state-of-theart
performance on computer vision tasks including semantic segmentation. However,
collecting and annotating a huge amount of data are needed to train CNNs.
Especially in the case of semantic segmentation, annotating pixel by pixel
takes a significant amount of time and often makes mistakes. In contrast,
computer graphics can generate a lot of accurate annotated data and easily
scale up by changing camera positions, textures and lights. Despite these
advantages, models trained on computer graphics datasets cannot perform well on
real data, which is known as the domain shift. To address this issue, we first
present that depth modal and synthetic noise are effective to reduce the domain
shift. Then, we develop the class-wise adaptation which obtains domain
invariant features of CNNs. To reduce the domain shift, we create computer
graphics rooms with a lot of props, and provide photo-realistic rendered
images.We also demonstrate the application which is combined semantic
segmentation with Simultaneous Localization and Mapping (SLAM). Our application
performs accurate 3D scene parsing in real-time on an actual room.
