Drawing-to-CAD Editing: An End-to-End Benchmark and a Reinforcement Learning Approach

Parametric computer-aided design (CAD) editing typically presumes access to underlying source programs. In practical engineering workflows, however, designers frequently need to modify components starting solely from dimensioned 2D engineering drawings and natural language instructions, without access to original CAD models. Existing benchmarks evaluate either drawing-to-CAD reconstruction or text-guided editing from provided source CAD models, leaving this end-to-end task unaddressed. Learning this task poses a fundamental coordination dilemma: two-stage cascaded pipelines suffer from severe error accumulation at inference, direct generation forfeits explicit intermediate geometric feedback, and joint learning with undifferentiated feedback can obscure component-specific quality differences and assign reconstruction credit to editing actions that cannot affect source quality. To tackle these challenges, we introduce CAD-D2E, the first benchmark for drawing-to-CAD editing. CAD-D2E pairs dimensioned orthographic views and textual instructions with executable source and edited programs across 15 edit subtypes and five part families, supporting end-to-end geometric evaluation and intermediate verification. We further propose JORE (Joint Optimization of Reconstruction and Editing), a framework that generates both programs within a unified autoregressive policy. JORE uses CARPO (Causal Advantage Routing for Policy Optimization) to route component-relative advantages according to autoregressive stage dependencies. On CAD-D2E, JORE achieves state-of-the-art performance, surpassing cascaded baselines and standard GRPO by substantial margins, with 78.50% IoU and 87.63% program executability. These results demonstrate the vital role of dependency-aware intermediate supervision in drawing-to-CAD editing.