  Deep learning methods are notoriously data-hungry, which requires a large
number of labeled samples. Unfortunately, the large amount of interactive
sample labeling efforts has dramatically hindered the application of deep
learning methods, especially for 3D modeling tasks, which require heterogeneous
samples. To alleviate the work of data annotation for learned 3D modeling of
fa\c{c}ades, this paper proposed a semi-supervised adversarial recognition
strategy embedded in inverse procedural modeling. Beginning with textured LOD-2
(Level-of-Details) models, we use the classical convolutional neural networks
to recognize the types and estimate the parameters of windows from image
patches. The window types and parameters are then assembled into procedural
grammar. A simple procedural engine is built inside an existing 3D modeling
software, producing fine-grained window geometries. To obtain a useful model
from a few labeled samples, we leverage the generative adversarial network to
train the feature extractor in a semi-supervised manner. The adversarial
training strategy can also exploit unlabeled data to make the training phase
more stable. Experiments using publicly available fa\c{c}ade image datasets
reveal that the proposed training strategy can obtain about 10% improvement in
classification accuracy and 50% improvement in parameter estimation under the
same network structure. In addition, performance gains are more pronounced when
testing against unseen data featuring different fa\c{c}ade styles.
