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Found 18 papers, 6 papers with code
Make-A-Shape: a Ten-Million-scale 3D Shape Model
We then make the representation generatable by a diffusion model by devising the subband coefficients packing scheme to layout the representation in a low-resolution grid.
SLiMe: Segment Like Me
Then, using the extracted attention maps, the text embeddings of Stable Diffusion are optimized such that, each of them, learn about a single segmented region from the training image.
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.
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.
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language
Recent works have demonstrated that natural language can be used to generate and edit 3D shapes.
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.
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.
UNIST: Unpaired Neural Implicit Shape Translation Network
We introduce UNIST, the first deep neural implicit model for general-purpose, unpaired shape-to-shape translation, in both 2D and 3D domains.
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.
Group-disentangled Representation Learning with Weakly-Supervised Regularization
Learning interpretable and human-controllable representations that uncover factors of variation in data remains an ongoing key challenge in representation learning.
CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation
Generating shapes using natural language can enable new ways of imagining and creating the things around us.
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.
CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly
We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies.
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
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.
Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning
We show that we can maximize the mutual information between 3D objects and their "chunks" to improve the representations in aligned datasets.
Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders
The latent variables learned by VAEs have seen considerable interest as an unsupervised way of extracting features, which can then be used for downstream tasks.
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.
