SPARC: Compiling Visual Assembly Intent into CAD Assemblies

Automatically assembling a set of existing CAD parts from a single reference image requires both visual reasoning and geometric verification. A vision-language model can infer part roles, relative layouts, and likely connections, but these predictions may not be supported by the actual part geometry. Conversely, B-Rep geometry can verify and realize assembly relations, yet often admits multiple plausible interface assignments. We present SPARC, a framework that requires no task-specific model training and formulates reference-guided CAD assembly as a compilation process from visual assembly intent to executable CAD constraints. Given an unordered set of B-Rep parts and a reference image, SPARC first generates a structured visual assembly proposal. Relation-specific geometric routines resolve feature references and construct candidate assembly plans, while assembly-level selection compares these plans using geometric evidence and cross-part consistency checks. Pre-execution preparation may further revise relations and interface bindings before compiling the resulting state into native CAD constraints. FreeCAD executes the program and returns execution and interference diagnostics for bounded local repair. We further introduce SPARC-Bench, a benchmark of 310 assemblies constructed from two public CAD assembly datasets. Experiments evaluate visual consistency, executed assembly-graph validity, interference, and end-to-end assembly success. SPARC achieves a 94.84% end-to-end assembly success rate on SPARC-Bench. Across the evaluated interface-ambiguity groups, the benefit of assembly-level plan replacement grows with ambiguity, supporting the need to resolve local geometric choices in the context of the complete assembly.