Vitruvion: A Generative Model of Parametric CAD Sketches

• URL: http://arxiv.org/abs/2109.14124v1
• Date: Wed, 29 Sep 2021 01:02:30 GMT
• Title: Vitruvion: A Generative Model of Parametric CAD Sketches
• Authors: Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams
• Abstract summary: We present an approach to generative modeling of parametric CAD sketches. Our model, trained on real-world designs from the SketchGraphs dataset, autoregressively synthesizes sketches as sequences of primitives. We condition the model on various contexts, including partial sketches (primers) and images of hand-drawn sketches.
• Score: 22.65229769427499
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards. The key characteristic of parametric CAD is that design intent is encoded not only via geometric primitives, but also by parameterized constraints between the elements. This relational specification can be viewed as the construction of a constraint program, allowing edits to coherently propagate to other parts of the design. Machine learning offers the intriguing possibility of accelerating the design process via generative modeling of these structures, enabling new tools such as autocompletion, constraint inference, and conditional synthesis. In this work, we present such an approach to generative modeling of parametric CAD sketches, which constitute the basic computational building blocks of modern mechanical design. Our model, trained on real-world designs from the SketchGraphs dataset, autoregressively synthesizes sketches as sequences of primitives, with initial coordinates, and constraints that reference back to the sampled primitives. As samples from the model match the constraint graph representation used in standard CAD software, they may be directly imported, solved, and edited according to downstream design tasks. In addition, we condition the model on various contexts, including partial sketches (primers) and images of hand-drawn sketches. Evaluation of the proposed approach demonstrates its ability to synthesize realistic CAD sketches and its potential to aid the mechanical design workflow.

Related papers

• CadVLM: Bridging Language and Vision in the Generation of Parametric CAD Sketches [24.239470848849418]
  Parametric Computer-Aided Design (CAD) is central to contemporary mechanical design. We propose CadVLM, an end-to-end vision language model for CAD generation.
  arXiv  Detail & Related papers  (2024-09-26T01:22:29Z)
• PS-CAD: Local Geometry Guidance via Prompting and Selection for CAD Reconstruction [86.726941702182]
  We introduce geometric guidance into the reconstruction network PS-CAD. We provide the geometry of surfaces where the current reconstruction differs from the complete model as a point cloud. Second, we use geometric analysis to extract a set of planar prompts, that correspond to candidate surfaces.
  arXiv  Detail & Related papers  (2024-05-24T03:43:55Z)
• Geometric Deep Learning for Computer-Aided Design: A Survey [85.79012726689511]
  This survey offers a comprehensive overview of learning-based methods in computer-aided design. It includes similarity analysis and retrieval, 2D and 3D CAD model synthesis, and CAD generation from point clouds. It provides a complete list of benchmark datasets and their characteristics, along with open-source codes that have propelled research in this domain.
  arXiv  Detail & Related papers  (2024-02-27T17:11:35Z)
• PPI-NET: End-to-End Parametric Primitive Inference [24.31083483088741]
  In engineering applications, line, circle, arc, and point are collectively referred to as primitives. We propose an efficient and accurate end-to-end method to infer parametric primitives from hand-drawn sketch images.
  arXiv  Detail & Related papers  (2023-08-03T03:50:49Z)
• SENS: Part-Aware Sketch-based Implicit Neural Shape Modeling [124.3266213819203]
  We present SENS, a novel method for generating and editing 3D models from hand-drawn sketches. S SENS analyzes the sketch and encodes its parts into ViT patch encoding. S SENS supports refinement via part reconstruction, allowing for nuanced adjustments and artifact removal.
  arXiv  Detail & Related papers  (2023-06-09T17:50:53Z)
• ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsing [46.778258706603005]
  This paper studies the problem of learning the shape given in the form of point clouds by inverse sketch-and-extrude. We present ExtrudeNet, an unsupervised end-to-end network for discovering sketch and extrude from point clouds.
  arXiv  Detail & Related papers  (2022-09-30T17:58:11Z)
• Using Machine Learning to Predict Engineering Technology Students' Success with Computer Aided Design [50.591267188664666]
  We show how data combined with machine learning techniques can predict how well a particular student will perform in a design task. We found that our models using early design sequence actions are particularly valuable for prediction. Further improvements to these models could lead to earlier predictions and thus provide students feedback sooner to enhance their learning.
  arXiv  Detail & Related papers  (2021-08-12T20:24:54Z)
• SketchGen: Generating Constrained CAD Sketches [34.26732809515799]
  We propose SketchGen as a generative model based on a transformer architecture to address the heterogeneity problem. A highlight of our work is the ability to produce primitives linked via constraints that enables the final output to be further regularized.
  arXiv  Detail & Related papers  (2021-06-04T20:45:03Z)
• Engineering Sketch Generation for Computer-Aided Design [10.732102570751392]
  We propose two generative models, CurveGen and TurtleGen, for engineering sketch generation. Both models generate curve primitives without the need for a sketch constraint solver. We find in our perceptual evaluation using human subjects that both CurveGen and TurtleGen produce more realistic engineering sketches.
  arXiv  Detail & Related papers  (2021-04-19T20:38:36Z)
• SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design [18.041056084458567]
  Parametric computer-aided design (CAD) is the dominant paradigm in mechanical engineering for physical design. SketchGraphs is a collection of 15 million sketches extracted from real-world CAD models coupled with an open-source data processing pipeline.
  arXiv  Detail & Related papers  (2020-07-16T17:56:25Z)
• CoSE: Compositional Stroke Embeddings [52.529172734044664]
  We present a generative model for complex free-form structures such as stroke-based drawing tasks. Our approach is suitable for interactive use cases such as auto-completing diagrams.
  arXiv  Detail & Related papers  (2020-06-17T15:22:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.