Attributed Feature Graphs (AFGs) represent CAD features as attributed nodes and relations as directed edges to enable GNN surrogate models that predict design performance with feature-level interpretability on the CarHoods10K dataset.
arXiv preprint arXiv:2401.15563 , year=
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Garment Particles is a 5D point cloud representation jointly encoding 2D sewing patterns and 3D geometry, supporting rectified flow generation from high-level inputs and diffusion-based editing of patterns or shapes.
MeshFlow uses a contrastive MeshVAE for compact mesh latents and a flow transformer for parallel generation, claiming 18x speedup over autoregressive methods with high accuracy on standard metrics.
GuideCAD generates 3D CAD models from text-image pairs via prefix embeddings in a pretrained LLM using a mapping network, achieving comparable quality with roughly 4x fewer parameters and 2x training efficiency than fine-tuning.
citing papers explorer
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Bridging CAD and Data-Driven Design: Attributed Feature Graphs for Engineering Design
Attributed Feature Graphs (AFGs) represent CAD features as attributed nodes and relations as directed edges to enable GNN surrogate models that predict design performance with feature-level interpretability on the CarHoods10K dataset.
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Garment Particles: A 2D--3D Symmetric Garment Representation for Generation and Editing
Garment Particles is a 5D point cloud representation jointly encoding 2D sewing patterns and 3D geometry, supporting rectified flow generation from high-level inputs and diffusion-based editing of patterns or shapes.
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MeshFlow: Efficient Artistic Mesh Generation via MeshVAE and Flow-based Diffusion Transformer
MeshFlow uses a contrastive MeshVAE for compact mesh latents and a flow transformer for parallel generation, claiming 18x speedup over autoregressive methods with high accuracy on standard metrics.
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GuideCAD: A Lightweight Multimodal Framework for 3D CAD Model Generation via Prefix Embedding
GuideCAD generates 3D CAD models from text-image pairs via prefix embeddings in a pretrained LLM using a mapping network, achieving comparable quality with roughly 4x fewer parameters and 2x training efficiency than fine-tuning.