Magic-Wall: Visualizing Room Decoration by Enhanced Wall Segmentation

This paper presents an intelligent system named Magic-wall, which enables visualization of the effect of room decoration automatically. Concretely, given an image of the indoor scene and a preferred color, the Magic-wall can automatically locate the wall regions in the image and smoothly replace the existing wall with the required one. The key idea of the proposed Magic-wall is to leverage visual semantics to guide the entire process of color substitution, including wall segmentation and replacement. To strengthen the reality of visualization, we make the following contributions. First, we propose an edge-aware fully convolutional neural network (Edge-aware-FCN) for indoor semantic scene parsing, in which a novel edge-prior branch is introduced to identify the boundary of different semantic regions better. To further polish the details between the wall and other semantic regions, we leverage the output of Edge-aware-FCN as the prior knowledge, concatenating with the image to form a new input for the Enhanced-Net. In such a case, the Enhanced-Net is able to capture more semantic-aware information from the input and polish some ambiguous regions. Finally, to naturally replace the color of the original walls, a simple yet effective color space conversion method is proposed for replacement with brightness reserved. We build a new indoor scene dataset upon ADE20K for training and testing, which includes six semantic labels. Extensive experimental evaluations and visualizations well demonstrate that the proposed Magic-wall is effective and can automatically generate a set of visually pleasing results.