Reinforcement Learning for Block Decomposition of CAD Models

• URL: http://arxiv.org/abs/2302.11066v1
• Date: Tue, 21 Feb 2023 23:43:19 GMT
• Title: Reinforcement Learning for Block Decomposition of CAD Models
• Authors: Benjamin C. DiPrete, Rao V. Garimella, Cristina Garcia Cardona, Navamita Ray
• Abstract summary: We present a novel AI-assisted method for decomposing (segmenting) planar CAD (computer-aided design) models into well shaped rectangular blocks. The blocks are required for generating good quality meshes suitable for numerical simulations of physical systems governed by conservation laws.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a novel AI-assisted method for decomposing (segmenting) planar CAD (computer-aided design) models into well shaped rectangular blocks as a proof-of-principle of a general decomposition method applicable to complex 2D and 3D CAD models. The decomposed blocks are required for generating good quality meshes (tilings of quadrilaterals or hexahedra) suitable for numerical simulations of physical systems governed by conservation laws. The problem of hexahedral mesh generation of general CAD models has vexed researchers for over 3 decades and analysts often spend more than 50% of the design-analysis cycle time decomposing complex models into simpler parts meshable by existing techniques. Our method uses reinforcement learning to train an agent to perform a series of optimal cuts on the CAD model that result in a good quality block decomposition. We show that the agent quickly learns an effective strategy for picking the location and direction of the cuts and maximizing its rewards as opposed to making random cuts. This paper is the first successful demonstration of an agent autonomously learning how to perform this block decomposition task effectively thereby holding the promise of a viable method to automate this challenging process.

Related papers

• Cliqueformer: Model-Based Optimization with Structured Transformers [102.55764949282906]
  We develop a model that learns the structure of an MBO task and empirically leads to improved designs. We evaluate Cliqueformer on various tasks, ranging from high-dimensional black-box functions to real-world tasks of chemical and genetic design.
  arXiv  Detail & Related papers  (2024-10-17T00:35:47Z)
• 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)
• Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment [69.33930972652594]
  We propose a novel structural pruning approach to jointly learn the weights and structurally prune architectures of CNN models. The core element of our method is a Reinforcement Learning (RL) agent whose actions determine the pruning ratios of the CNN model's layers. We conduct the joint training and pruning by iteratively training the model's weights and the agent's policy.
  arXiv  Detail & Related papers  (2024-03-28T15:22:29Z)
• Make-A-Shape: a Ten-Million-scale 3D Shape Model [52.701745578415796]
  This paper introduces Make-A-Shape, a new 3D generative model designed for efficient training on a vast scale. We first innovate a wavelet-tree representation to compactly encode shapes by formulating the subband coefficient filtering scheme. We derive the subband adaptive training strategy to train our model to effectively learn to generate coarse and detail wavelet coefficients.
  arXiv  Detail & Related papers  (2024-01-20T00:21:58Z)
• Point2CAD: Reverse Engineering CAD Models from 3D Point Clouds [26.10631058349939]
  We propose a hybrid analytic-neural reconstruction scheme that bridges the gap between segmented point clouds and structured CAD models. We also propose a novel implicit neural representation of freeform surfaces, driving up the performance of our overall CAD reconstruction scheme.
  arXiv  Detail & Related papers  (2023-12-07T08:23:44Z)
• Learning Versatile 3D Shape Generation with Improved AR Models [91.87115744375052]
  Auto-regressive (AR) models have achieved impressive results in 2D image generation by modeling joint distributions in the grid space. We propose the Improved Auto-regressive Model (ImAM) for 3D shape generation, which applies discrete representation learning based on a latent vector instead of volumetric grids.
  arXiv  Detail & Related papers  (2023-03-26T12:03:18Z)
• Optimizing CAD Models with Latent Space Manipulation [4.180840853105103]
  We extend StyleCLIP to work with CAD models in the form of voxel models. We demonstrate the ability of our system for the optimiziation of automation-related features by optimizing the grabability of various CAD models.
  arXiv  Detail & Related papers  (2023-03-09T08:25:09Z)
• Cut-and-Approximate: 3D Shape Reconstruction from Planar Cross-sections with Deep Reinforcement Learning [0.0]
  We present to the best of our knowledge the first 3D shape reconstruction network to solve this task. Our method is based on applying a Reinforcement Learning algorithm to learn how to effectively parse the shape.
  arXiv  Detail & Related papers  (2022-10-22T17:48:12Z)
• Automated LoD-2 Model Reconstruction from Very-HighResolution Satellite-derived Digital Surface Model and Orthophoto [1.2691047660244335]
  We propose a model-driven method that reconstructs LoD-2 building models following a "decomposition-optimization-fitting" paradigm. Our proposed method has addressed a few technical caveats over existing methods, resulting in practically high-quality results.
  arXiv  Detail & Related papers  (2021-09-08T19:03:09Z)
• Neural Subdivision [58.97214948753937]
  This paper introduces Neural Subdivision, a novel framework for data-driven coarseto-fine geometry modeling. We optimize for the same set of network weights across all local mesh patches, thus providing an architecture that is not constrained to a specific input mesh, fixed genus, or category. We demonstrate that even when trained on a single high-resolution mesh our method generates reasonable subdivisions for novel shapes.
  arXiv  Detail & Related papers  (2020-05-04T20:03:21Z)

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.