33 citations · 109 across the 23 of their papers we have counts for
15 papers · 1 filter
Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data
Mohammadmehdi Ataei, Farzaneh Askari, Kamal Rahimi Malekshan +1
Computer-Aided Design (CAD) models are defined by their construction history: a parametric recipe that encodes design intent. However, existing large-scale 3D datasets predominantl…
AutoBrep: Autoregressive B-Rep Generation with Unified Topology and Geometry
Xiang Xu, Pradeep Kumar Jayaraman, Joseph G. Lambourne +3
The boundary representation (B-Rep) is the standard data structure used in Computer-Aided Design (CAD) for defining solid models. Despite recent progress, directly generating B-Rep…
B-Rep Distance Functions (BR-DF): How to Represent a B-Rep Model by Volumetric Distance Functions?
Fuyang Zhang, Pradeep Kumar Jayaraman, Xiang Xu +1
This paper presents a novel geometric representation for CAD Boundary Representation (B-Rep) based on volumetric distance functions, dubbed B-Rep Distance Functions (BR-DF). BR-DF…
3D-WAG: Hierarchical Wavelet-Guided Autoregressive Generation for High-Fidelity 3D Shapes
Tejaswini Medi, Arianna Rampini, Pradyumna Reddy +2
Autoregressive (AR) models have achieved remarkable success in natural language and image generation, but their application to 3D shape modeling remains largely unexplored. Unlike…
CadVLM: Bridging Language and Vision in the Generation of Parametric CAD Sketches
Sifan Wu, Amir Khasahmadi, Mor Katz +4
Parametric Computer-Aided Design (CAD) is central to contemporary mechanical design. However, it encounters challenges in achieving precise parametric sketch modeling and lacks pra…
BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry
Xiang Xu, Joseph G. Lambourne, Pradeep Kumar Jayaraman +3
This paper presents BrepGen, a diffusion-based generative approach that directly outputs a Boundary representation (B-rep) Computer-Aided Design (CAD) model. BrepGen represents a B…
Sketch-A-Shape: Zero-Shot Sketch-to-3D Shape Generation
Aditya Sanghi, Pradeep Kumar Jayaraman, Arianna Rampini +4
Significant progress has recently been made in creative applications of large pre-trained models for downstream tasks in 3D vision, such as text-to-shape generation. This motivates…
Hierarchical Neural Coding for Controllable CAD Model Generation
Xiang Xu, Pradeep Kumar Jayaraman, Joseph G. Lambourne +2
This paper presents a novel generative model for Computer Aided Design (CAD) that 1) represents high-level design concepts of a CAD model as a three-level hierarchical tree of neur…
Reconstructing editable prismatic CAD from rounded voxel models
Joseph G. Lambourne, Karl D. D. Willis, Pradeep Kumar Jayaraman +3
Reverse Engineering a CAD shape from other representations is an important geometric processing step for many downstream applications. In this work, we introduce a novel neural net…
SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks
Xiang Xu, Karl D. D. Willis, Joseph G. Lambourne +3
We present SkexGen, a novel autoregressive generative model for computer-aided design (CAD) construction sequences containing sketch-and-extrude modeling operations. Our model util…
UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Reps
Peter Meltzer, Hooman Shayani, Amir Khasahmadi +3
Boundary Representations (B-Reps) are the industry standard in 3D Computer Aided Design/Manufacturing (CAD/CAM) and industrial design due to their fidelity in representing stylisti…
RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers
Saeid Asgari Taghanaki, Jieliang Luo, Ran Zhang +3
The 3D deep learning community has seen significant strides in pointcloud processing over the last few years. However, the datasets on which deep models have been trained have larg…
PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing
Saeid Asgari Taghanaki, Kaveh Hassani, Pradeep Kumar Jayaraman +2
Deep classifiers tend to associate a few discriminative input variables with their objective function, which in turn, may hurt their generalization capabilities. To address this, o…
UV-Net: Learning from Boundary Representations
Pradeep Kumar Jayaraman, Aditya Sanghi, Joseph G. Lambourne +4
We introduce UV-Net, a novel neural network architecture and representation designed to operate directly on Boundary representation (B-rep) data from 3D CAD models. The B-rep forma…
How Powerful Are Randomly Initialized Pointcloud Set Functions?
Aditya Sanghi, Pradeep Kumar Jayaraman
We study random embeddings produced by untrained neural set functions, and show that they are powerful representations which well capture the input features for downstream tasks su…