Visual-to-Executable Procedural Reconstruction of Buildings.

Procedural generation is well-suited for modeling 3D buildings, as architectural structures are naturally composed of repetitive and parameterized components. It provides a compact and editable representation by encoding geometry through executable rules. However, constructing such procedural representations typically relies on manually designed grammars, making them difficult to obtain in practice. We study image-based procedural facade reconstruction, where the goal is to recover an editable building procedural representation from an in-the-wild building image. We propose a hierarchical neuro-symbolic framework that decomposes the reconstruction into two coupled levels: a global structure layout tree for split-and-repeat facade organization, and a component parameterization representation for local architectural. A fine-tuned layout model recovers and refines the global structure through rendered visual feedback, while a tile-level predictor maps representative component crops to structured procedural attributes. The two representations are then deterministically compiled into executable CGA programs. Experiments show that our method achieves strong performance on both regular facades and challenging in-the-wild images, while maintaining high structural consistency and procedural executability. In addition, the tile-level predictor improves fine-grained architectural detail, enabling accurate and controllable component synthesis.