A multimodal benchmark for editable constraint preserving history based CAD modeling

Abstract

Editable CAD generation requires access to procedural history rather than final geometry alone, yet widely used CAD datasets typically omit explicit geometric constraints, native parametric assets, or rich semantic labels. We introduce EPICCAD, a multimodal benchmark for history-based CAD modeling that aligns compact constraint-aware modeling sequences with multi-view renderings, STEP B-reps, native parametric files, and textual annotations. Importantly, EPICCAD flattens only the redundant sketch-loop nesting inside individual profiles; it retains the procedural feature history and reconstructs loop topology at execution time, so geometric relations are made explicit through constraints rather than discarded. EPICCAD combines 152,360 academic samples with 8141 industrial Siemens NX parts, yielding 160,501 models that better reflect real design complexity. We further present AM $$_\text {EPICCAD}$$ , an annotation pipeline that parses loop structure, inter-part relations, and operation histories from CAD sequences and then uses a large language model to generate process-aware, geometry-aware, and function-aware descriptions. Experiments show that EPICCAD’s flat sequence representation reduces token length while preserving geometric fidelity, explicit constraints substantially improve editability, and both the annotation module and industrial split strengthen text-to-CAD generation. EPICCAD therefore provides a practical foundation for AI systems that must generate, interpret, and revise executable CAD models.