  3D modeling of non-linear objects from stylized sketches is a challenge even
for experts in Computer Graphics (CG). The extrapolation of objects parameters
from a stylized sketch is a very complex and cumbersome task. In the present
study, we propose a broker system that mediates between the modeler and the 3D
modelling software and can transform a stylized sketch of a tree into a
complete 3D model. The input sketches do not need to be accurate or detailed,
and only need to represent a rudimentary outline of the tree that the modeler
wishes to 3D-model. Our approach is based on a well-defined Deep Neural Network
(DNN) architecture, we called TreeSketchNet (TSN), based on convolutions and
able to generate Weber and Penn parameters that can be interpreted by the
modelling software to generate a 3D model of a tree starting from a simple
sketch. The training dataset consists of Synthetically-Generated
\revision{(SG)} sketches that are associated with Weber-Penn parameters
generated by a dedicated Blender modelling software add-on. The accuracy of the
proposed method is demonstrated by testing the TSN with both synthetic and
hand-made sketches. Finally, we provide a qualitative analysis of our results,
by evaluating the coherence of the predicted parameters with several
distinguishing features.
