CAD Reconstruction
9 papers with code • 3 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in CAD Reconstruction
| Trend | Dataset | Best Model | Paper | Code | Compare |
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DeepCAD
Fusion 360 Gallery
CC3D
Text2CAD
Most implemented papers
End-to-End CAD Model Retrieval and 9DoF Alignment in 3D Scans
alexeybokhovkin/CAD-Deform
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ICCV 2019
We present a novel, end-to-end approach to align CAD models to an 3D scan of a scene, enabling transformation of a noisy, incomplete 3D scan to a compact, CAD reconstruction with clean, complete object geometry.
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences
AutodeskAILab/Fusion360GalleryDataset
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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.
DeepCAD: A Deep Generative Network for Computer-Aided Design Models
ChrisWu1997/DeepCAD
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ICCV 2021
We present the first 3D generative model for a drastically different shape representation --- describing a shape as a sequence of computer-aided design (CAD) operations.
ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generation
We view the reconstruction of CAD models in the boundary representation (B-Rep) as the detection of geometric primitives of different orders, i. e. vertices, edges and surface patches, and the correspondence of primitives, which are holistically modeled as a chain complex, and show that by modeling such comprehensive structures more complete and regularized reconstructions can be achieved.
ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsing
This paper studies the problem of learning the shape given in the form of point clouds by inverse sketch-and-extrude.
SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operations
bunnysocrazy/secad-net
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CVPR 2023
Reverse engineering CAD models from raw geometry is a classic but strenuous research problem.
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.
Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Prompts
We propose Text2CAD, the first AI framework for generating text-to-parametric CAD models using designer-friendly instructions for all skill levels.
CAD-Recode: Reverse Engineering CAD Code from Point Clouds
Taking advantage of the exposure of pre-trained Large Language Models (LLMs) to Python code, we leverage a relatively small LLM as a decoder for CAD-Recode and combine it with a lightweight point cloud projector.
