research-article

Reconstructing editable prismatic CAD from rounded voxel models

Authors:

Joseph George Lambourne,

Pradeep Kumar Jayaraman,

Kamal Rahimi MalekshanAuthors Info & Claims

SA '22: SIGGRAPH Asia 2022 Conference Papers

Article No.: 53, Pages 1 - 9

https://doi.org/10.1145/3550469.3555424

Published: 30 November 2022 Publication History

Abstract

Reverse Engineering a CAD shape from other representations is an important geometric processing step for many downstream applications. In this work, we introduce a novel neural network architecture to solve this challenging task and approximate a smoothed signed distance function with an editable, constrained, prismatic CAD model. During training, our method reconstructs the input geometry in the voxel space by decomposing the shape into a series of 2D profile images and 1D envelope functions. These can then be recombined in a differentiable way allowing a geometric loss function to be defined. During inference, we obtain the CAD data by first searching a database of 2D constrained sketches to find curves which approximate the profile images, then extrude them and use Boolean operations to build the final CAD model. Our method approximates the target shape more closely than other methods and outputs highly editable constrained parametric sketches which are compatible with existing CAD software.

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Zhang YHe FFan RFan B(2024)View2CAD: Parsing Multi-view into CAD Command Sequences2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD61410.2024.10580755(2949-2954)Online publication date: 8-May-2024
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Jung MKim MKim J(2024)ContrastCAD: Contrastive Learning-Based Representation Learning for Computer-Aided Design ModelsIEEE Access10.1109/ACCESS.2024.341581612(84830-84842)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3415816
Zhang SGuan ZJiang HNing TWang XTan P(2024)Brep2Seq: a dataset and hierarchical deep learning network for reconstruction and generation of computer-aided design modelsJournal of Computational Design and Engineering10.1093/jcde/qwae00511:1(110-134)Online publication date: 19-Jan-2024
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Index Terms

Reconstructing editable prismatic CAD from rounded voxel models
1. Applied computing
  1. Physical sciences and engineering
    1. Engineering
      1. Computer-aided design
2. Computing methodologies
  1. Computer graphics
    1. Shape modeling
      1. Parametric curve and surface models
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

SA '22: SIGGRAPH Asia 2022 Conference Papers

November 2022

482 pages

ISBN:9781450394703

DOI:10.1145/3550469

Editors:
Soon Ki Jung
Kyungpook National University, South Korea
,
Jehee Lee
Seoul National University, South Korea
,
Adam Bargteil
University of Maryland Baltimore County, USA

Copyright © 2022 ACM.

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Published: 30 November 2022

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presentation https://dl.acm.org/doi/10.1145/3550469.3555424#3550469.3555424.mp4

"Appendix", "Video showing profile and CAD interpolation" https://dl.acm.org/doi/10.1145/3550469.3555424#Reconstructing_editable_prismatic_CAD_from_rounded_voxel_models_interpolation_video.mp4

"Appendix", "Video showing profile and solid interpolation" https://dl.acm.org/doi/10.1145/3550469.3555424#Reconstructing_editable_prismatic_CAD_from_rounded_voxel_models_interpolation_video.mp4

"Appendix", "Video showing profile and CAD interpolation" https://dl.acm.org/doi/10.1145/3550469.3555424#Reconstructing_editable_prismatic_CAD_from_rounded_voxel_models_appendix.pdf

"Appendix", "Video showing profile and solid interpolation" https://dl.acm.org/doi/10.1145/3550469.3555424#Reconstructing_editable_prismatic_CAD_from_rounded_voxel_models_appendix.pdf

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SA '22

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SIGGRAPH

SA '22: SIGGRAPH Asia 2022

December 6 - 9, 2022

Daegu, Republic of Korea

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Overall Acceptance Rate 178 of 869 submissions, 20%

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Cited By

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https://doi.org/10.1109/CSCWD61410.2024.10580755
Jung MKim MKim J(2024)ContrastCAD: Contrastive Learning-Based Representation Learning for Computer-Aided Design ModelsIEEE Access10.1109/ACCESS.2024.341581612(84830-84842)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3415816
Zhang SGuan ZJiang HNing TWang XTan P(2024)Brep2Seq: a dataset and hierarchical deep learning network for reconstruction and generation of computer-aided design modelsJournal of Computational Design and Engineering10.1093/jcde/qwae00511:1(110-134)Online publication date: 19-Jan-2024
https://doi.org/10.1093/jcde/qwae005
Yu FChen QTanveer MAmiri AZhang HOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)D2CSGProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667111(22807-22819)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667111
Xu XJayaraman PLambourne JWillis KFurukawa YKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Hierarchical neural coding for controllable CAD model generationProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3620009(38443-38461)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3620009
Jones BNoeckel JKodnongbua MBaran ISchulz A(2023)B-rep Matching for Collaborating Across CAD SystemsACM Transactions on Graphics10.1145/359212542:4(1-13)Online publication date: 26-Jul-2023
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Li YLiu SYang XGuo JGuo JGuo Y(2023)Surface and Edge Detection for Primitive Fitting of Point CloudsACM SIGGRAPH 2023 Conference Proceedings10.1145/3588432.3591522(1-10)Online publication date: 23-Jul-2023
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Ritchie DGuerrero PJones RMitra NSchulz AWillis KWu J(2023)Neurosymbolic Models for Computer GraphicsComputer Graphics Forum10.1111/cgf.1477542:2(545-568)Online publication date: 23-May-2023
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Mallis DAli SDupont ECherenkova KKaradeniz AKhan MKacem AGusev GAouada D(2023)SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)10.1109/ICCVW60793.2023.00194(1778-1787)Online publication date: 2-Oct-2023
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Li PGuo JZhang XYan D(2023)SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operations2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.01613(16816-16826)Online publication date: Jun-2023
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