RealCAD: Towards Real-World Image-to-CAD Reconstruction under Domain Shift and Parameter Bias
Reconstructing editable Computer-Aided Design (CAD) models from images is essential for downstream modification, manufacturing, and design reuse. However, existing image-to-CAD methods are developed predominantly on synthetic renderings and face two coupled obstacles: a substantial appearance domain gap between synthetic and real images, and a previously overlooked parameter bias in widely used CAD data. We show that the local normalization adopted by DeepCAD concentrates several geometric parameters around a few discrete values while encoding substantial information in a single scale factor.
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “RealCAD: Towards Real-World Image-to-CAD Reconstruction unde” ≈ “Tongyi-MAI/Z-Image-Turbo””
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- LinkedLinked via arxiv author · 85%Yihe Sun →
“RealCAD: Towards Real-World Image-to-CAD Reconstruction under Domain Shift and Parameter Bias”
- LinkedLinked via arxiv author · 85%Ziyu Lu →
“RealCAD: Towards Real-World Image-to-CAD Reconstruction under Domain Shift and Parameter Bias”
- LinkedLinked via arxiv author · 85%Kaihua Tang →
“RealCAD: Towards Real-World Image-to-CAD Reconstruction under Domain Shift and Parameter Bias”
- LinkedLinked via arxiv author · 85%Xian-Sheng Hua →
“RealCAD: Towards Real-World Image-to-CAD Reconstruction under Domain Shift and Parameter Bias”