5 papers
ConCAD: Constraint-Aware Image-to-CAD Generation with Dual-Granularity Rewards
Chenxi Zhai, Xi Cheng, Hang Cheng +5
Image-to-CAD generation seeks executable parametric programs that recover both the geometry and design intent of a reference object. Existing systems are commonly evaluated by vali…
DIP-CAD: A Design-Intent-Preserving Agent Harness for Parametric CAD Modeling
Xi Cheng, Chenxi Zhai, Hang Cheng +3
Parametric CAD modeling requires not only matching the target shape, but also preserving design intent under parameter edits. However, most existing methods focus on improving stat…
SDGraph: Multi-Level Sketch Representation Learning by Sparse-Dense Graph Architecture
Xi Cheng, Pingfa Feng, Mingyu Fan +3
Freehand sketches exhibit unique sparsity and abstraction, necessitating learning pipelines distinct from those designed for images. For sketch learning methods, the central object…
Constraint-Aware Feature Learning for Parametric Point Cloud
Xi Cheng, Ruiqi Lei, Di Huang +5
Parametric point clouds are sampled from CAD shapes and are becoming increasingly common in industrial manufacturing. Most CAD-specific deep learning methods focus on geometric fea…
Training-Free Point Cloud Recognition Based on Geometric and Semantic Information Fusion
Yan Chen, Di Huang, Zhichao Liao +3
The trend of employing training-free methods for point cloud recognition is becoming increasingly popular due to its significant reduction in computational resources and time costs…