ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design Models

• URL: http://arxiv.org/abs/2404.01645v1
• Date: Tue, 2 Apr 2024 05:30:39 GMT
• Title: ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design Models
• Authors: Minseop Jung, Minseong Kim, Jibum Kim,
• Abstract summary: We propose a contrastive learning-based approach to learning CAD models, named ContrastCAD. ContrastCAD effectively captures semantic information within the construction sequences of the CAD model. We also propose a new CAD data augmentation method, called a Random Replace and Extrude (RRE) method, to enhance the learning performance of the model.
• Score: 0.7373617024876725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The success of Transformer-based models has encouraged many researchers to learn CAD models using sequence-based approaches. However, learning CAD models is still a challenge, because they can be represented as complex shapes with long construction sequences. Furthermore, the same CAD model can be expressed using different CAD construction sequences. We propose a novel contrastive learning-based approach, named ContrastCAD, that effectively captures semantic information within the construction sequences of the CAD model. ContrastCAD generates augmented views using dropout techniques without altering the shape of the CAD model. We also propose a new CAD data augmentation method, called a Random Replace and Extrude (RRE) method, to enhance the learning performance of the model when training an imbalanced training CAD dataset. Experimental results show that the proposed RRE augmentation method significantly enhances the learning performance of Transformer-based autoencoders, even for complex CAD models having very long construction sequences. The proposed ContrastCAD model is shown to be robust to permutation changes of construction sequences and performs better representation learning by generating representation spaces where similar CAD models are more closely clustered. Our codes are available at https://github.com/cm8908/ContrastCAD.

Related papers

• Image2CADSeq: Computer-Aided Design Sequence and Knowledge Inference from Product Images [0.7673339435080445]
  In scenarios where digital CAD files are not accessible, reverse engineering (RE) has been used to reconstruct 3D CAD models. Recent advances have seen the rise of data-driven approaches for RE, with a primary focus on converting 3D data, such as point clouds, into 3D models in boundary representation (B-rep) format. Our research introduces a novel data-driven approach with an Image2CADSeq neural network model.
  arXiv  Detail & Related papers  (2025-01-09T02:36:21Z)
• BlenderLLM: Training Large Language Models for Computer-Aided Design with Self-improvement [45.19076032719869]
  We present BlenderLLM, a framework for training Large Language Models (LLMs) in Computer-Aided Design (CAD) Our results reveal that existing models demonstrate significant limitations in generating accurate CAD scripts. Through minimal instruction-based fine-tuning and iterative self-improvement, BlenderLLM significantly surpasses these models in both functionality and accuracy of CAD script generation.
  arXiv  Detail & Related papers  (2024-12-16T14:34:02Z)
• 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)
• CAD-MLLM: Unifying Multimodality-Conditioned CAD Generation With MLLM [39.113795259823476]
  We introduce the CAD-MLLM, the first system capable of generating parametric CAD models conditioned on the multimodal input. We use advanced large language models (LLMs) to align the feature space across diverse multi-modalities data and CAD models' vectorized representations. Our resulting dataset, named Omni-CAD, is the first multimodal CAD dataset that contains textual description, multi-view images, points, and command sequence for each CAD model.
  arXiv  Detail & Related papers  (2024-11-07T18:31:08Z)
• 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)
• 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)
• SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operations [21.000539206470897]
  SECAD-Net is an end-to-end neural network aimed at reconstructing compact and easy-to-edit CAD models. We show superiority over state-of-the-art alternatives including the closely related method for supervised CAD reconstruction.
  arXiv  Detail & Related papers  (2023-03-19T09:26:03Z)
• 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)
• Patch2CAD: Patchwise Embedding Learning for In-the-Wild Shape Retrieval from a Single Image [58.953160501596805]
  We propose a novel approach towards constructing a joint embedding space between 2D images and 3D CAD models in a patch-wise fashion. Our approach is more robust than state of the art in real-world scenarios without any exact CAD matches.
  arXiv  Detail & Related papers  (2021-08-20T20:58:52Z)
• DeepCAD: A Deep Generative Network for Computer-Aided Design Models [37.655225142981564]
  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. Drawing an analogy between CAD operations and natural language, we propose a CAD generative network based on the Transformer.
  arXiv  Detail & Related papers  (2021-05-20T03:29:18Z)

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.