arXiv Computer Vision
MeshCone: Second-Order Cone Programming for Geometrically-Constrained Mesh Enhancement
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CADFit: Precise Mesh-to-CAD Program Generation with Hybrid Optimization
arXiv:2605. 01171v2 Announce Type: replace-cross Abstract: Despite recent progress, recovering parametric CAD construction sequences from geometric input, such as meshes or point clouds, is a key challenge for design and manufacturing, as existing CAD reconstruction and generation methods are largely restricted to difficult-to-edit formats like meshes or Breps or editable simple sketch-and-extrude pipelines and low-complexity datasets.
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CADFather: Autonomous CAD Reconstruction through Coordinated Tool Use
Reconstructing an editable CAD model from a 3D shape remains a challenging engineering task. Existing methods can propose CAD operations, but no single source of proposals works equally well across di...
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CADFather: Autonomous CAD Reconstruction through Coordinated Tool Use
CADFather is an autonomous system that reconstructs editable CAD models from 3D meshes by coordinating vision‑language assistants, learned and algorithmic tools, and numerical optimization. It evaluates and refines candidate CAD programs, maintaining alternatives for each part and preserving the best valid result throughout reconstruction. The system uses pretrained models without additional training and is evaluated on multiple benchmark datasets for reconstruction quality and execution validity.
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Geometry-Guided Modeling of Foundation Features Enables Generalizable Object Shape Deformation Learning
The paper introduces a generalizable deformation learning framework that reconstructs 3D objects by deforming a category-level shape template to match a monocular observation. It employs a geometry-guided feature modeling mechanism to enrich foundation features with template topology, creating a geometry-aware representation that is explicitly correlated with the target observation for precise deformation. A view-adaptive feature aggregation module further bridges the gap between the fixed template and arbitrary target views by leveraging multi-view template features and camera poses, ensuring robust feature alignment across diverse viewpoints.
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CADKnitter: Compositional CAD Generation from Text and Geometry Guidance
CADKnitter is a compositional CAD generation framework that uses geometric-guiding cues to steer diffusion sampling, enabling the creation of complementary CAD parts that satisfy both geometric constraints of an existing model and semantic constraints from a text prompt. The authors introduce KnitCAD, a dataset of over 310,000 CAD models paired with textual prompts and assembly metadata to support training and evaluation. Experiments show that CADKnitter outperforms state‑of‑the‑art baselines by a clear margin.
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CADReasoner: Iterative Program Editing for CAD Reverse Engineering
CADReasoner is a model that iteratively refines its CAD reconstruction by comparing the input shape with the predicted mesh, outputting a runnable CadQuery Python program. It fuses multi‑view renders and point clouds and uses a scan‑simulation protocol to bridge realism gaps during training and evaluation. The approach achieves state‑of‑the‑art performance on DeepCAD, Fusion 360, and MCB benchmarks for both clean and scan‑sim tracks.