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DeepJoin: Learning a Joint Occupancy, Signed Distance, and Normal Field Function for Shape Repair

Authors:

Natasha Kholgade BanerjeeAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 41, Issue 6

Article No.: 230, Pages 1 - 10

https://doi.org/10.1145/3550454.3555470

Published: 30 November 2022 Publication History

Abstract

We introduce DeepJoin, an automated approach to generate high-resolution repairs for fractured shapes using deep neural networks. Existing approaches to perform automated shape repair operate exclusively on symmetric objects, require a complete proxy shape, or predict restoration shapes using low-resolution voxels which are too coarse for physical repair. We generate a high-resolution restoration shape by inferring a corresponding complete shape and a break surface from an input fractured shape. We present a novel implicit shape representation for fractured shape repair that combines the occupancy function, signed distance function, and normal field. We demonstrate repairs using our approach for synthetically fractured objects from ShapeNet, 3D scans from the Google Scanned Objects dataset, objects in the style of ancient Greek pottery from the QP Cultural Heritage dataset, and real fractured objects. We outperform six baseline approaches in terms of chamfer distance and normal consistency. Unlike existing approaches and restorations generated using subtraction, DeepJoin restorations do not exhibit surface artifacts and join closely to the fractured region of the fractured shape. Our code is available at: https://github.com/Terascale-All-sensing-Research-Studio/DeepJoin.

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Index Terms

DeepJoin: Learning a Joint Occupancy, Signed Distance, and Normal Field Function for Shape Repair
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
      1. Shape analysis
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 41, Issue 6

December 2022

1428 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3550454

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Published: 30 November 2022

Published in TOG Volume 41, Issue 6

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Cited By

Henrich PGyenes BScheikl PNeumann GMathis-Ullrich F(2024)Registered and Segmented Deformable Object Reconstruction from a Single View Point Cloud2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00310(3117-3126)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00310
Bhunia ALi CBilen H(2024)Looking 3D: Anomaly Detection with 2D-3D Alignment2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01634(17263-17272)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01634
Chen DYu MLi QHe XLiu FShen J(2024)Precise tooth design using deep learning-based templatesJournal of Dentistry10.1016/j.jdent.2024.104971144(104971)Online publication date: May-2024
https://doi.org/10.1016/j.jdent.2024.104971
Ming YMe RChen JRahmat R(2023)A Systematic Review on Virtual Reality Technology for Ancient Ceramic RestorationApplied Sciences10.3390/app1315899113:15(8991)Online publication date: 5-Aug-2023
https://doi.org/10.3390/app13158991
Song XLamb NBanerjee SBanerjee N(2023)Reinforcement-Learning Based Robotic Assembly of Fractured Objects Using Visual and Tactile Information2023 9th International Conference on Automation, Robotics and Applications (ICARA)10.1109/ICARA56516.2023.10125938(170-174)Online publication date: 10-Feb-2023
https://doi.org/10.1109/ICARA56516.2023.10125938
Lamb NPalmer CMolloy BBanerjee SBanerjee N(2023)Fantastic Breaks: A Dataset of Paired 3D Scans of Real-World Broken Objects and Their Complete Counterparts2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.00454(4681-4691)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.00454

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