CADFlow: Recovering the Construction Process for Sketch-to-CAD Generation

Generating accurate and executable parametric CAD models from raster sketch images remains challenging. Existing methods either rely on individually accessible strokes and learn to group them into CAD operations, requiring additional stroke recovery for raster sketch inputs, or directly generate CAD sequences from raster sketch images, where insufficient intermediate-state context increases the risk of erroneous construction steps and invalid geometries. To address this problem, we propose CADFlow, which formulates sketch-to-CAD generation as construction-process recovery. CADFlow employs CAD Construction Process Recovery (CCPR) to recursively recover preceding-state features from the final input state to an empty state. It then decodes adjacent-state transitions in the forward direction into a parametric CAD sequence. To alleviate error accumulation, CADFlow further employs Error-Aware Redrawing (EAR) to detect and locally redraw erroneous CAD operations and optimize sketch parameters under predicted geometric constraints. Experiments on DeepCAD and Fusion 360 demonstrate that CADFlow generates accurate, executable, and geometrically consistent CAD models while exhibiting strong cross-dataset generalization.