Inverse Procedural Modeling of Branching Structures by Inferring L-Systems

Jianwei Guo, Haiyong Jiang, Bedrich Benes, Oliver Deussen, Xiaopeng Zhang, Dani Lischinski, Hui Huang

Abstract:

We introduce an inverse procedural modeling approach that learns Lsystem representations of pixel images with branching structures. Our
fully automatic model generates a compact set of textual rewriting rules
that describe the input. We use deep learning to discover atomic structures
such as line segments or branchings. Orientation and scaling of these structures are determined and the detected structures are combined into a tree.
The initial representation is analyzed, and repeating parts are encoded into
a small grammar by using greedy optimization while the user can control
the size of the detected rules. The output is an L-system that represents
the input image as a simple text and a set of terminal symbols. We apply
our approach to a variety of examples, demonstrate its robustness against
noise and blur, and we show that it can detect user sketches and complex
input structures.