  Researches of analysis and parsing around fa\c{c}ades to enrich the 3D
feature of fa\c{c}ade models by semantic information raised some attention in
the community, whose main idea is to generate higher resolution components with
similar shapes and textures to increase the overall resolution at the expense
of reconstruction accuracy. While this approach works well for components like
windows and doors, there is no solution for fa\c{c}ade background at present.
In this paper, we introduce the concept of representative region texture, which
can be used in the above modeling approach by tiling the representative texture
around the fa\c{c}ade region, and propose a semi-supervised way to do
representative region texture extraction from a fa\c{c}ade image. Our method
does not require any additional labelled data to train as long as the semantic
information is given, while a traditional end-to-end model requires plenty of
data to increase its performance. Our method can extract texture from any
repetitive images, not just fa\c{c}ade, which is not capable in an end-to-end
model as it relies on the distribution of training set. Clustering with
weighted distance is introduced to further increase the robustness to noise or
an imprecise segmentation, and make the extracted texture have a higher
resolution and more suitable for tiling. We verify our method on various
fa\c{c}ade images, and the result shows our method has a significant
performance improvement compared to only a random crop on fa\c{c}ade. We also
demonstrate some application scenarios and proposed a fa\c{c}ade modeling
workflow with the representative region texture, which has a better visual
resolution for a regular fa\c{c}ade.
