HistCAD: A Constraint-Aware Parametric History-Based CAD Representation, Dataset, and Benchmark with Industrial Complexity
Parametric CAD sequences are reusable because dimensional and geometric constraints govern how parameter changes propagate. Existing CAD generation datasets and benchmarks emphasize reconstruction fidelity, execution validity, or static shape similarity, leaving preservation of design intent under edits largely unmeasured. We introduce HistCAD, a representation standard, dataset, and benchmark for executable parametric CAD with explicit constraints. HistCAD defines an intermediate language independent of CAD software, recording sketch primitives, constraints, feature operations, and 3D point boundary references for operations such as fillet and chamfer. The dataset contains 170,236 executable sequences aligned with native CAD models, STEP files, rendered views, and text annotations, combining academic scale with professionally authored industrial complexity. Building on this representation, the Constraint-Aware Editability Benchmark applies parameter edits and reports Edit Reachability, conditional preserved constraint satisfaction, and Overall Editable Success, abbreviated ER, cPCSR, and OES; these metrics separate failures to reach a valid edited state from failures to preserve required constraints. Experiments show that explicit constraints are essential for preserving design intent after edits, and that HistCAD supports supervised CAD generation from text and direct LLM workflows. We argue that HistCAD reframes CAD generation from static shape imitation to the synthesis of reusable parametric sequences with explicit constraints.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Semi-parametric Makeup Transfer via Semantic-aware Correspondence
The large discrepancy between the source non-makeup image and the reference makeup image is one of the key challenges in makeup transfer. Conventional approaches for makeup transfer either learn disentangled representati…
DecoderGoG: Relation-aware Graph-over-Graph Network for Visual Dialog
Visual dialog, which aims to hold a meaningful conversation with humans about a given image, is a challenging task that requires models to reason the complex dependencies among visual content, dialog history, and current…
coreference-resolutionCoreference ResolutionImplicit RelationsRelation+1Temporal Knowledge Graph Reasoning Triggered by Memories
Inferring missing facts in temporal knowledge graphs is a critical task and has been widely explored. Extrapolation in temporal reasoning tasks is more challenging and gradually attracts the attention of researchers sinc…
AttributeDecision MakingKnowledge GraphsRepresentation LearningKinaema: a recurrent sequence model for memory and pose in motion
One key aspect of spatially aware robots is the ability to "find their bearings", ie. to correctly situate themselves in previously seen spaces. In this work, we focus on this particular scenario of continuous robotics o…
Leveraging Multivariate Long-Term History Representation for Time Series Forecasting
Multivariate Time Series (MTS) forecasting has a wide range of applications in both industry and academia. Recent advances in Spatial-Temporal Graph Neural Network (STGNN) have achieved great progress in modelling spatia…
Graph Neural NetworkTime SeriesTime Series Forecasting