Self-Supervised Representation Learning for CAD

• URL: http://arxiv.org/abs/2210.10807v1
• Date: Wed, 19 Oct 2022 18:00:18 GMT
• Title: Self-Supervised Representation Learning for CAD
• Authors: Benjamin T. Jones, Michael Hu, Vladimir G. Kim, Adriana Schulz
• Abstract summary: This work proposes to leverage unlabeled CAD geometry on supervised learning tasks. We learn a novel, hybrid implicit/explicit surface representation for B-Rep geometry.
• Score: 19.5326204665895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The design of man-made objects is dominated by computer aided design (CAD) tools. Assisting design with data-driven machine learning methods is hampered by lack of labeled data in CAD's native format; the parametric boundary representation (B-Rep). Several data sets of mechanical parts in B-Rep format have recently been released for machine learning research. However, large scale databases are largely unlabeled, and labeled datasets are small. Additionally, task specific label sets are rare, and costly to annotate. This work proposes to leverage unlabeled CAD geometry on supervised learning tasks. We learn a novel, hybrid implicit/explicit surface representation for B-Rep geometry, and show that this pre-training significantly improves few-shot learning performance and also achieves state-of-the-art performance on several existing B-Rep benchmarks.

Related papers

• BRep Boundary and Junction Detection for CAD Reverse Engineering [14.662769071664252]
  In machining process, 3D reverse engineering of the mechanical system is an integral, highly important, and yet time consuming step. Deep learning-based Scan-to-CAD modeling can offer designers enormous editability to quickly modify CAD model. We propose a supervised boundary representation (BRep) detection network BRepDetNet from 3D scans of CC3D and ABC dataset.
  arXiv  Detail & Related papers  (2024-09-21T09:53:11Z)
• GenQA: Generating Millions of Instructions from a Handful of Prompts [67.54980063851605]
  Most public instruction finetuning datasets are relatively small compared to the closed source datasets used to train industry models. In this work, we study methods for generating large instruction datasets from a single prompt. Our dataset meets or exceeds both WizardLM and Ultrachat on both knowledge-intensive leaderboard tasks as well as conversational evaluations.
  arXiv  Detail & Related papers  (2024-06-14T17:44:08Z)
• Self-supervised Graph Neural Network for Mechanical CAD Retrieval [29.321027284348272]
  GC-CAD is a self-supervised contrastive graph neural network-based method for mechanical CAD retrieval. The proposed method achieves significant accuracy improvements and up to 100 times efficiency improvement over the baseline methods.
  arXiv  Detail & Related papers  (2024-06-13T06:56:49Z)
• PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning [49.60634126342945]
  Counterfactually Augmented Data (CAD) involves creating new data samples by applying minimal yet sufficient modifications to flip the label of existing data samples to other classes. Recent research reveals that training with CAD may lead models to overly focus on modified features while ignoring other important contextual information. We employ contrastive learning to promote global feature alignment in addition to learning counterfactual clues.
  arXiv  Detail & Related papers  (2024-06-09T07:29:55Z)
• 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)
• 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)
• CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations [17.051792180335354]
  This paper proposes a new deep neural network, CADOps-Net, that jointly learns the CAD operation types and the decomposition into different CAD operation steps. Compared to existing datasets, the complexity and variety of CC3D-Ops models are closer to those used for industrial purposes.
  arXiv  Detail & Related papers  (2022-08-22T19:12:20Z)
• Multi-Task Self-Training for Learning General Representations [97.01728635294879]
  Multi-task self-training (MuST) harnesses the knowledge in independent specialized teacher models to train a single general student model. MuST is scalable with unlabeled or partially labeled datasets and outperforms both specialized supervised models and self-supervised models when training on large scale datasets.
  arXiv  Detail & Related papers  (2021-08-25T17:20:50Z)
• Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation [58.64720318755764]
  Semi-Supervised Learning (SSL) has seen success in many application domains, but this success often hinges on the availability of task-specific unlabeled data. Knowledge distillation (KD) has enabled compressing deep networks and ensembles, achieving the best results when distilling knowledge on fresh task-specific unlabeled examples. We present a general framework called "generate, annotate, and learn (GAL)" that uses unconditional generative models to synthesize in-domain unlabeled data.
  arXiv  Detail & Related papers  (2021-06-11T05:01:24Z)
• SB-GCN: Structured BREP Graph Convolutional Network for Automatic Mating of CAD Assemblies [3.732457298487595]
  Assembly modeling is not directly applicable to modern CAD systems because it eschews the dominant data structure of modern CAD: parametric boundary representations (BREPs) We propose SB-GCN, a representation learning scheme on BREPs that retains the topological structure of parts, and use these learned representations to predict CAD type mates.
  arXiv  Detail & Related papers  (2021-05-25T22:07:55Z)
• UV-Net: Learning from Boundary Representations [17.47054752280569]
  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. B-rep data presents some unique challenges when used with modern machine learning due to the complexity of the data structure and its support for both continuous non-Euclidean geometric entities and discrete topological entities.
  arXiv  Detail & Related papers  (2020-06-18T00:12:52Z)

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.