Search Results for author:
Found 15 papers, 7 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.
Sketch-A-Shape: Zero-Shot Sketch-to-3D Shape Generation
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.
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.
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.
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.
UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Reps
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 stylistic details.
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
RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers
We also find that robustness to unseen transformations cannot be brought about merely by extensive data augmentation.
PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing
We show that coupling a PointMask layer with an arbitrary model can discern the points in the input space which contribute the most to the prediction score, thereby leading to interpretability.
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.
How Powerful Are Randomly Initialized Pointcloud Set Functions?
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 such as classification, and are often linearly separable.
Quadtree Convolutional Neural Networks
Specifically, the computational and memory costs in QCNN grow linearly in the number of non-zero pixels, as opposed to traditional CNNs where the costs are quadratic in the number of pixels.
