Search Results for author:
Found 14 papers, 6 papers with code
BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry
Starting from the root and progressing to the leaf, BrepGen employs Transformer-based diffusion models to sequentially denoise node features while duplicated nodes are detected and merged, recovering the B-Rep topology information.
ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility
The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together.
TExplain: Explaining Learned Visual Features via Pre-trained (Frozen) Language Models
These sentences are then used to extract the most frequent words, providing a comprehensive understanding of the learned features and patterns within the classifier.
Hierarchical Neural Coding for Controllable CAD Model Generation
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 neural codes, from global part arrangement down to local curve geometry; and 2) controls the generation or completion of CAD models by specifying the target design using a code tree.
Ranked #6 on
CAD Reconstruction
on Fusion 360 Gallery
Next Steps for Human-Centered Generative AI: A Technical Perspective
Through iterative, cross-disciplinary discussions, we define and propose next-steps for Human-centered Generative AI (HGAI).
Reconstructing editable prismatic CAD from rounded voxel models
Reverse Engineering a CAD shape from other representations is an important geometric processing step for many downstream applications.
Ranked #5 on
CAD Reconstruction
on Fusion 360 Gallery
SimCURL: Simple Contrastive User Representation Learning from Command Sequences
Our method introduces a user-session network architecture, as well as session dropout as a novel way of data augmentation.
SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks
We present SkexGen, a novel autoregressive generative model for computer-aided design (CAD) construction sequences containing sketch-and-extrude modeling operations.
SolidGen: An Autoregressive Model for Direct B-rep Synthesis
Key to achieving this is our Indexed Boundary Representation that references B-rep vertices, edges and faces in a well-defined hierarchy to capture the geometric and topological relations suitable for use with machine learning.
JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints
Physical products are often complex assemblies combining a multitude of 3D parts modeled in computer-aided design (CAD) software.
Engineering Sketch Generation for Computer-Aided Design
Engineering sketches form the 2D basis of parametric Computer-Aided Design (CAD), the foremost modeling paradigm for manufactured objects.
BRepNet: A topological message passing system for solid models
Boundary representation (B-rep) models are the standard way 3D shapes are described in Computer-Aided Design (CAD) applications.
Ranked #1 on
B-Rep face segmentation
on Fusion 360 Gallery
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences
Parametric computer-aided design (CAD) is a standard paradigm used to design manufactured objects, where a 3D shape is represented as a program supported by the CAD software.
UV-Net: Learning from Boundary Representations
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.
