PPI-NET: End-to-End Parametric Primitive Inference

• URL: http://arxiv.org/abs/2308.01521v1
• Date: Thu, 3 Aug 2023 03:50:49 GMT
• Title: PPI-NET: End-to-End Parametric Primitive Inference
• Authors: Liang Wang and Xiaogang Wang
• Abstract summary: 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.
• Score: 24.31083483088741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In engineering applications, line, circle, arc, and point are collectively referred to as primitives, and they play a crucial role in path planning, simulation analysis, and manufacturing. When designing CAD models, engineers typically start by sketching the model's orthographic view on paper or a whiteboard and then translate the design intent into a CAD program. Although this design method is powerful, it often involves challenging and repetitive tasks, requiring engineers to perform numerous similar operations in each design. To address this conversion process, we propose an efficient and accurate end-to-end method that avoids the inefficiency and error accumulation issues associated with using auto-regressive models to infer parametric primitives from hand-drawn sketch images. Since our model samples match the representation format of standard CAD software, they can be imported into CAD software for solving, editing, and applied to downstream design tasks.

Related papers

• Text2CAD: Text to 3D CAD Generation via Technical Drawings [45.3611544056261]
  Text2CAD is a novel framework that employs stable diffusion models tailored to automate the generation process. We show that Text2CAD effectively generates technical drawings that are accurately translated into high-quality 3D CAD models.
  arXiv  Detail & Related papers  (2024-11-09T15:12:06Z)
• 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)
• Img2CAD: Reverse Engineering 3D CAD Models from Images through VLM-Assisted Conditional Factorization [12.12975824816803]
  Reverse engineering 3D computer-aided design (CAD) models from images is an important task for many downstream applications. In this work, we introduce a novel approach that conditionally factorizes the task into two sub-problems. We propose TrAssembler that conditioned on the discrete structure with semantics predicts the continuous attribute values.
  arXiv  Detail & Related papers  (2024-07-19T06:53:30Z)
• 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)
• AutoCAD: Automatically Generating Counterfactuals for Mitigating Shortcut Learning [70.70393006697383]
  We present AutoCAD, a fully automatic and task-agnostic CAD generation framework. In this paper, we present AutoCAD, a fully automatic and task-agnostic CAD generation framework.
  arXiv  Detail & Related papers  (2022-11-29T13:39:53Z)
• 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)
• Reconstructing editable prismatic CAD from rounded voxel models [16.03976415868563]
  We introduce a novel neural network architecture to solve this challenging task. Our method reconstructs the input geometry in the voxel space by decomposing the shape. During inference, we obtain the CAD data by first searching a database of 2D constrained sketches.
  arXiv  Detail & Related papers  (2022-09-02T16:44:10Z)
• Vitruvion: A Generative Model of Parametric CAD Sketches [22.65229769427499]
  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.
  arXiv  Detail & Related papers  (2021-09-29T01:02:30Z)
• 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)
• 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)

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.