Generating implicit object fragment datasets for machine learning
One of the primary challenges inherent in utilizing deep learning models is the scarcity and accessibility hurdles associated with acquiring datasets of sufficient size to facilitate effective training of these networks. This is particularly significant in object detection, shape completion, and fra...
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| Published in: | Computers & graphics Vol. 125; p. 104104 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.12.2024
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| ISSN: | 0097-8493 |
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| Abstract | One of the primary challenges inherent in utilizing deep learning models is the scarcity and accessibility hurdles associated with acquiring datasets of sufficient size to facilitate effective training of these networks. This is particularly significant in object detection, shape completion, and fracture assembly. Instead of scanning a large number of real-world fragments, it is possible to generate massive datasets with synthetic pieces. However, realistic fragmentation is computationally intensive in the preparation (e.g., pre-factured models) and generation. Otherwise, simpler algorithms such as Voronoi diagrams provide faster processing speeds at the expense of compromising realism. In this context, it is required to balance computational efficiency and realism. This paper introduces a GPU-based framework for the massive generation of voxelized fragments derived from high-resolution 3D models, specifically prepared for their utilization as training sets for machine learning models. This rapid pipeline enables controlling how many pieces are produced, their dispersion and the appearance of subtle effects such as erosion. We have tested our pipeline with an archaeological dataset, producing more than 1M fragmented pieces from 1,052 Iberian vessels (Github). Although this work primarily intends to provide pieces as implicit data represented by voxels, triangle meshes and point clouds can also be inferred from the initial implicit representation. To underscore the unparalleled benefits of CPU and GPU acceleration in generating vast datasets, we compared against a realistic fragment generator that highlights the potential of our approach, both in terms of applicability and processing time. We also demonstrate the synergies between our pipeline and realistic simulators, which frequently cannot select the number and size of resulting pieces. To this end, a deep learning model was trained over realistic fragments and our dataset, showing similar results.
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•GPU-based fragmentation of voxelized artefacts for simulating brittle fractures.•Generation of large datasets for training machine learning models.•Definition of how many fragments are produced, their dispersion and appearance.•Publication of a dataset comprising more than 1M fragments. |
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| AbstractList | One of the primary challenges inherent in utilizing deep learning models is the scarcity and accessibility hurdles associated with acquiring datasets of sufficient size to facilitate effective training of these networks. This is particularly significant in object detection, shape completion, and fracture assembly. Instead of scanning a large number of real-world fragments, it is possible to generate massive datasets with synthetic pieces. However, realistic fragmentation is computationally intensive in the preparation (e.g., pre-factured models) and generation. Otherwise, simpler algorithms such as Voronoi diagrams provide faster processing speeds at the expense of compromising realism. In this context, it is required to balance computational efficiency and realism. This paper introduces a GPU-based framework for the massive generation of voxelized fragments derived from high-resolution 3D models, specifically prepared for their utilization as training sets for machine learning models. This rapid pipeline enables controlling how many pieces are produced, their dispersion and the appearance of subtle effects such as erosion. We have tested our pipeline with an archaeological dataset, producing more than 1M fragmented pieces from 1,052 Iberian vessels (Github). Although this work primarily intends to provide pieces as implicit data represented by voxels, triangle meshes and point clouds can also be inferred from the initial implicit representation. To underscore the unparalleled benefits of CPU and GPU acceleration in generating vast datasets, we compared against a realistic fragment generator that highlights the potential of our approach, both in terms of applicability and processing time. We also demonstrate the synergies between our pipeline and realistic simulators, which frequently cannot select the number and size of resulting pieces. To this end, a deep learning model was trained over realistic fragments and our dataset, showing similar results.
[Display omitted]
•GPU-based fragmentation of voxelized artefacts for simulating brittle fractures.•Generation of large datasets for training machine learning models.•Definition of how many fragments are produced, their dispersion and appearance.•Publication of a dataset comprising more than 1M fragments. |
| ArticleNumber | 104104 |
| Author | López, Alfonso Segura, Rafael J. Navarro, Pablo Ogayar, Carlos J. Fuertes, José M. Rueda, Antonio J. |
| Author_xml | – sequence: 1 givenname: Alfonso orcidid: 0000-0003-1423-9496 surname: López fullname: López, Alfonso email: allopezr@ujaen.es organization: Department of Computer Science, Campus Las Lagunillas s/n, Jaén, 23071, Spain – sequence: 2 givenname: Antonio J. orcidid: 0000-0001-7692-454X surname: Rueda fullname: Rueda, Antonio J. email: ajrueda@ujaen.es organization: Department of Computer Science, Campus Las Lagunillas s/n, Jaén, 23071, Spain – sequence: 3 givenname: Rafael J. orcidid: 0000-0002-3075-6963 surname: Segura fullname: Segura, Rafael J. email: rsegura@ujaen.es organization: Department of Computer Science, Campus Las Lagunillas s/n, Jaén, 23071, Spain – sequence: 4 givenname: Carlos J. orcidid: 0000-0003-0958-990X surname: Ogayar fullname: Ogayar, Carlos J. email: cogayar@ujaen.es organization: Department of Computer Science, Campus Las Lagunillas s/n, Jaén, 23071, Spain – sequence: 5 givenname: Pablo orcidid: 0000-0003-2180-449X surname: Navarro fullname: Navarro, Pablo email: pnavarro@cenpat-conicet.gob.ar organization: Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico, CONICET, Puerto Madryn, Argentina – sequence: 6 givenname: José M. orcidid: 0000-0001-6624-4102 surname: Fuertes fullname: Fuertes, José M. email: jmf@ujaen.es organization: Department of Computer Science, Campus Las Lagunillas s/n, Jaén, 23071, Spain |
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| Keywords | GPU programming Fracture dataset Voxel Voronoi Fragmentation |
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| References | Gregor, Bauer, Sipiran, Perakis, Schreck (b14) 2015 Deng, Jiang, Chen, Zhang, Yao, Song, Sun, Yang, Yan, Huang, Bajaj (b20) 2023; 113 Behley J, Garbade M, Milioto A, Quenzel J, Behnke S, Stachniss C, Gall J. SemanticKITTI: A dataset for semantic scene understanding of lidar sequences. In: 2019 IEEE/CVF international conference on computer vision (ICCV). 2019, p. 9296–306. Lucena, Fuertes, Martínez-Carrillo, Ruiz, Carrascosa (b3) 2017; 76 URL . ISSN: 1045-0823. Graciano, Rueda, Pospíšil, Bittner, Benes (b40) 2021; 27 Fan, Chitalu, Komura (b7) 2022; 41 Müller, Chentanez, Kim (b10) 2013; 32 Lamb, Banerjee, Banerjee (b13) 2022; 41 URL . Conference Name: 2021 IEEE/CVF International Conference on Computer Vision (ICCV) ISBN: 9781665428125 Place: Montreal, QC, Canada Publisher: IEEE. Cheng Y-C, Lee H-Y, Tulyakov S, Schwing AG, Gui L-Y. SDFusion: Multimodal 3D shape completion, reconstruction, and generation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023, p. 4456–65, URL. Muguercia, Bosch, Patow (b1) 2014; 45 Skibo, Schiffer (b37) 1987; 14 Ogayar-Anguita, Rueda-Ruiz, Segura-Sánchez, Díaz-Medina, García-Fernández (b35) 2020; 8 Garland, Heckbert (b38) 1997 Hahn, Wojtan (b6) 2016; 35 Velić, May, Moresi (b2) 2009; 8 URL . ISSN: 2380-7504. Zhang, Garcia, Xu, Shao, Yang (b36) 2018; 100 Blender (b9) 2024 Chen, Choy, Savva, Chang, Funkhouser, Savarese (b32) 2018 Schvartzman, Otaduy (b16) 2014 Huang J, Zhan G, Fan Q, Mo K, Shao L, Chen B, Guibas L, Dong H. Generative 3D part assembly via dynamic graph learning. In: The IEEE conference on neural information processing systems. neurIPS, 2020, p. 1–12. Zafar, Stephens, Larsson, Sakaguchi, Clive, Sampath, Museth, Blakey, Gazdik, Thomas (b12) 2010 Sellán, Luong, Mattos Da Silva, Ramakrishnan, Yang, Jacobson (b34) 2023; 42 Stutz, Geiger (b19) 2020; 128 Sellán, Chen, Wu, Garg, Jacobson (b17) 2022 Yu, Rao, Wang, Liu, Lu, Zhou (b21) 2021 Payne A, Limp F. Virtual hampson museum project. Tsukiyama, Kondo, Kakuse, Saba, Ozaki, Itoh (b39) 1986 URL . Accepted: 2014-12-16T07:33:42Z ISSN: 1727-5288. Koschier D, Lipponer S, Bender J. Adaptive tetrahedral meshes for brittle fracture simulation. The Eurographics Association; ISBN: 978-3-905674-61-3, 2014 Museth (b15) 2021 Zobeidi, Atanasov (b25) 2021 Manivasagam, Wang, Wong, Zeng, Sazanovich, Tan, Yang, Ma, Urtasun (b30) 2020 Oh, Shin, Jun (b11) 2012; 23 Tang J, Lei J, Xu D, Ma F, Jia K, Zhang L. SA-ConvONet: Sign-agnostic optimization of convolutional occupancy networks. In: 2021 IEEE/CVF International Conference on Computer Vision. ICCV, 2021, p. 6484–93. Chitalu, Miao, Subr, Komura (b8) 2020; 39 Gu, Ma, Manivasagam, Zeng, Wang, Xiong, Su, Urtasun (b18) 2020 O’Brien, Bargteil, Hodgins (b4) 2002; 21 Kerbl, Kopanas, Leimkühler, Drettakis (b22) 2023; 42 Zheng, Yu, Dai, Liu (b24) 2021 Choi, Zhou, Miller, Koltun (b29) 2016 Navarro P, Cintas C, Lucena M, Fuertes JM, Rueda A, Segura R, Ogayar-Anguita C, González-José R, Delrieux C. IberianVoxel: Automatic completion of iberian ceramics for cultural heritage studies. In: Thirty-second international joint conference on artificial intelligence. Vol. 6, 2023, p. 5833–41. Koutsoudis, Pavlidis, Arnaoutoglou, Tsiafakis, Chamzas (b33) 2009; 10 Blender (10.1016/j.cag.2024.104104_b9) 2024 Manivasagam (10.1016/j.cag.2024.104104_b30) 2020 Müller (10.1016/j.cag.2024.104104_b10) 2013; 32 Oh (10.1016/j.cag.2024.104104_b11) 2012; 23 Kerbl (10.1016/j.cag.2024.104104_b22) 2023; 42 10.1016/j.cag.2024.104104_b31 10.1016/j.cag.2024.104104_b5 Gregor (10.1016/j.cag.2024.104104_b14) 2015 Chen (10.1016/j.cag.2024.104104_b32) 2018 Ogayar-Anguita (10.1016/j.cag.2024.104104_b35) 2020; 8 Skibo (10.1016/j.cag.2024.104104_b37) 1987; 14 Deng (10.1016/j.cag.2024.104104_b20) 2023; 113 Sellán (10.1016/j.cag.2024.104104_b17) 2022 Sellán (10.1016/j.cag.2024.104104_b34) 2023; 42 Muguercia (10.1016/j.cag.2024.104104_b1) 2014; 45 Koutsoudis (10.1016/j.cag.2024.104104_b33) 2009; 10 Garland (10.1016/j.cag.2024.104104_b38) 1997 Graciano (10.1016/j.cag.2024.104104_b40) 2021; 27 Lucena (10.1016/j.cag.2024.104104_b3) 2017; 76 Schvartzman (10.1016/j.cag.2024.104104_b16) 2014 Museth (10.1016/j.cag.2024.104104_b15) 2021 Yu (10.1016/j.cag.2024.104104_b21) 2021 Zhang (10.1016/j.cag.2024.104104_b36) 2018; 100 Tsukiyama (10.1016/j.cag.2024.104104_b39) 1986 Gu (10.1016/j.cag.2024.104104_b18) 2020 10.1016/j.cag.2024.104104_b41 Zafar (10.1016/j.cag.2024.104104_b12) 2010 10.1016/j.cag.2024.104104_b23 Chitalu (10.1016/j.cag.2024.104104_b8) 2020; 39 10.1016/j.cag.2024.104104_b26 Stutz (10.1016/j.cag.2024.104104_b19) 2020; 128 10.1016/j.cag.2024.104104_b27 10.1016/j.cag.2024.104104_b28 Velić (10.1016/j.cag.2024.104104_b2) 2009; 8 Lamb (10.1016/j.cag.2024.104104_b13) 2022; 41 Zobeidi (10.1016/j.cag.2024.104104_b25) 2021 O’Brien (10.1016/j.cag.2024.104104_b4) 2002; 21 Hahn (10.1016/j.cag.2024.104104_b6) 2016; 35 Fan (10.1016/j.cag.2024.104104_b7) 2022; 41 Zheng (10.1016/j.cag.2024.104104_b24) 2021 Choi (10.1016/j.cag.2024.104104_b29) 2016 |
| References_xml | – reference: Koschier D, Lipponer S, Bender J. Adaptive tetrahedral meshes for brittle fracture simulation. The Eurographics Association; ISBN: 978-3-905674-61-3, 2014, – volume: 128 start-page: 1162 year: 2020 end-page: 1181 ident: b19 article-title: Learning 3D shape completion under weak supervision publication-title: Int J Comput Vis – volume: 42 year: 2023 ident: b22 article-title: 3D Gaussian splatting for real-time radiance field rendering publication-title: ACM Trans Graph (SIGGRAPH Conf Proc) – volume: 8 start-page: 343 year: 2009 end-page: 355 ident: b2 article-title: A fast robust algorithm for computing discrete voronoi diagrams publication-title: J Math Model Algorithms – reference: , URL . ISSN: 1045-0823. – volume: 41 start-page: 177:1 year: 2022 end-page: 177:20 ident: b7 article-title: Simulating brittle fracture with material points publication-title: ACM Trans Graph – year: 2021 ident: b25 article-title: A deep signed directional distance function for object shape representation – volume: 42 start-page: 10:1 year: 2023 end-page: 10:12 ident: b34 article-title: Breaking good: Fracture modes for realtime destruction publication-title: ACM Trans Graph – volume: 41 start-page: 65 year: 2022 end-page: 78 ident: b13 article-title: MendNet: Restoration of fractured shapes using learned occupancy functions publication-title: Comput Graph Forum – reference: Navarro P, Cintas C, Lucena M, Fuertes JM, Rueda A, Segura R, Ogayar-Anguita C, González-José R, Delrieux C. IberianVoxel: Automatic completion of iberian ceramics for cultural heritage studies. In: Thirty-second international joint conference on artificial intelligence. Vol. 6, 2023, p. 5833–41. – volume: 21 start-page: 291 year: 2002 end-page: 294 ident: b4 article-title: Graphical modeling and animation of ductile fracture publication-title: ACM Trans Graph – volume: 100 start-page: 61 year: 2018 end-page: 70 ident: b36 article-title: Efficient voxelization using projected optimal scanline publication-title: Graph Models – start-page: 15 year: 2014 end-page: 22 ident: b16 article-title: Fracture animation based on high-dimensional Voronoi diagrams publication-title: Proceedings of the 18th meeting of the ACM SIGGRAPH symposium on interactive 3D graphics and games – start-page: 209 year: 1997 end-page: 216 ident: b38 article-title: Surface simplification using quadric error metrics publication-title: Proceedings of the 24th annual conference on computer graphics and interactive techniques – reference: , URL . Accepted: 2014-12-16T07:33:42Z ISSN: 1727-5288. – start-page: 7 year: 2015 end-page: 14 ident: b14 article-title: Automatic 3D object fracturing for evaluation of partial retrieval and object restoration tasks - benchmark and application to 3D cultural heritage data publication-title: 3DOR – year: 2018 ident: b32 article-title: Text2Shape: Generating shapes from natural language by learning joint embeddings – start-page: 1 year: 2021 end-page: 197 ident: b15 article-title: OPENVDB publication-title: ACM SIGGRAPH 2021 courses – volume: 23 year: 2012 ident: b11 article-title: Practical simulation of hierarchical brittle fracture publication-title: Comput. Animat. Virtual Worlds – reference: Tang J, Lei J, Xu D, Ma F, Jia K, Zhang L. SA-ConvONet: Sign-agnostic optimization of convolutional occupancy networks. In: 2021 IEEE/CVF International Conference on Computer Vision. ICCV, 2021, p. 6484–93. – start-page: 12498 year: 2021 end-page: 12507 ident: b21 article-title: PoinTr: Diverse point cloud completion with geometry-aware transformers publication-title: ICCV – reference: Huang J, Zhan G, Fan Q, Mo K, Shao L, Chen B, Guibas L, Dong H. Generative 3D part assembly via dynamic graph learning. In: The IEEE conference on neural information processing systems. neurIPS, 2020, p. 1–12. – volume: 39 start-page: 569 year: 2020 end-page: 583 ident: b8 article-title: Displacement-Correlated XFEM for simulating brittle fracture publication-title: Comput Graph Forum – reference: Cheng Y-C, Lee H-Y, Tulyakov S, Schwing AG, Gui L-Y. SDFusion: Multimodal 3D shape completion, reconstruction, and generation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023, p. 4456–65, URL. – start-page: 1 year: 2010 ident: b12 article-title: Destroying LA for ”2012” publication-title: ACM SIGGRAPH 2010 talks – reference: , URL . ISSN: 2380-7504. – volume: 32 start-page: 115:1 year: 2013 end-page: 115:10 ident: b10 article-title: Real time dynamic fracture with volumetric approximate convex decompositions publication-title: ACM Trans Graph – year: 1986 ident: b39 article-title: Method and system for data compression and restoration – year: 2016 ident: b29 article-title: A large dataset of object scans – volume: 8 start-page: 12675 year: 2020 end-page: 12687 ident: b35 article-title: A GPU-based framework for generating implicit datasets of voxelized polygonal models for the training of 3D convolutional neural networks publication-title: IEEE Access – year: 2021 ident: b24 article-title: Deep implicit templates for 3D shape representation – reference: Payne A, Limp F. Virtual hampson museum project. – volume: 113 start-page: 102 year: 2023 end-page: 112 ident: b20 article-title: TAssembly: Data-driven fractured object assembly using a linear template model publication-title: Comput Graph – volume: 10 start-page: 281 year: 2009 end-page: 295 ident: b33 article-title: Qp: A tool for generating 3D models of ancient Greek pottery publication-title: J. Cult. Herit. – start-page: 283 year: 2020 end-page: 299 ident: b18 article-title: Weakly-supervised 3D shape completion in the wild publication-title: Computer vision – ECCV 2020 – reference: Behley J, Garbade M, Milioto A, Quenzel J, Behnke S, Stachniss C, Gall J. SemanticKITTI: A dataset for semantic scene understanding of lidar sequences. In: 2019 IEEE/CVF international conference on computer vision (ICCV). 2019, p. 9296–306. – year: 2022 ident: b17 article-title: Breaking Bad: A dataset for geometric fracture and reassembly – reference: , URL . Conference Name: 2021 IEEE/CVF International Conference on Computer Vision (ICCV) ISBN: 9781665428125 Place: Montreal, QC, Canada Publisher: IEEE. – start-page: 11164 year: 2020 end-page: 11173 ident: b30 article-title: LiDARsim: Realistic LiDAR simulation by leveraging the real world publication-title: 2020 IEEE/CVF conference on computer vision and pattern recognition – volume: 45 start-page: 86 year: 2014 end-page: 100 ident: b1 article-title: Fracture modeling in computer graphics publication-title: Comput Graph – volume: 14 start-page: 83 year: 1987 end-page: 96 ident: b37 article-title: The effects of water on processes of ceramic abrasion publication-title: J Archaeol Sci – year: 2024 ident: b9 article-title: Cell fracture: Blender 4.1 manual – volume: 27 start-page: 3733 year: 2021 end-page: 3744 ident: b40 article-title: QuadStack: An efficient representation and direct rendering of layered datasets publication-title: IEEE Trans Vis Comput Graphics – volume: 76 start-page: 21565 year: 2017 end-page: 21577 ident: b3 article-title: Classification of archaeological pottery profiles using modal analysis publication-title: Multimedia Tools Appl – volume: 35 start-page: 104:1 year: 2016 end-page: 104:11 ident: b6 article-title: Fast approximations for boundary element based brittle fracture simulation publication-title: ACM Trans Graph – volume: 14 start-page: 83 issue: 1 year: 1987 ident: 10.1016/j.cag.2024.104104_b37 article-title: The effects of water on processes of ceramic abrasion publication-title: J Archaeol Sci doi: 10.1016/S0305-4403(87)80008-0 – year: 1986 ident: 10.1016/j.cag.2024.104104_b39 – ident: 10.1016/j.cag.2024.104104_b5 – volume: 42 issue: 4 year: 2023 ident: 10.1016/j.cag.2024.104104_b22 article-title: 3D Gaussian splatting for real-time radiance field rendering publication-title: ACM Trans Graph (SIGGRAPH Conf Proc) – start-page: 15 year: 2014 ident: 10.1016/j.cag.2024.104104_b16 article-title: Fracture animation based on high-dimensional Voronoi diagrams – volume: 10 start-page: 281 issue: 2 year: 2009 ident: 10.1016/j.cag.2024.104104_b33 article-title: Qp: A tool for generating 3D models of ancient Greek pottery publication-title: J. Cult. Herit. doi: 10.1016/j.culher.2008.07.012 – ident: 10.1016/j.cag.2024.104104_b23 doi: 10.24963/ijcai.2023/647 – year: 2024 ident: 10.1016/j.cag.2024.104104_b9 – year: 2021 ident: 10.1016/j.cag.2024.104104_b24 – volume: 32 start-page: 115:1 issue: 4 year: 2013 ident: 10.1016/j.cag.2024.104104_b10 article-title: Real time dynamic fracture with volumetric approximate convex decompositions publication-title: ACM Trans Graph doi: 10.1145/2461912.2461934 – volume: 128 start-page: 1162 issue: 5 year: 2020 ident: 10.1016/j.cag.2024.104104_b19 article-title: Learning 3D shape completion under weak supervision publication-title: Int J Comput Vis doi: 10.1007/s11263-018-1126-y – volume: 35 start-page: 104:1 issue: 4 year: 2016 ident: 10.1016/j.cag.2024.104104_b6 article-title: Fast approximations for boundary element based brittle fracture simulation publication-title: ACM Trans Graph doi: 10.1145/2897824.2925902 – year: 2016 ident: 10.1016/j.cag.2024.104104_b29 – volume: 39 start-page: 569 issue: 2 year: 2020 ident: 10.1016/j.cag.2024.104104_b8 article-title: Displacement-Correlated XFEM for simulating brittle fracture publication-title: Comput Graph Forum doi: 10.1111/cgf.13953 – volume: 45 start-page: 86 year: 2014 ident: 10.1016/j.cag.2024.104104_b1 article-title: Fracture modeling in computer graphics publication-title: Comput Graph doi: 10.1016/j.cag.2014.08.006 – start-page: 12498 year: 2021 ident: 10.1016/j.cag.2024.104104_b21 article-title: PoinTr: Diverse point cloud completion with geometry-aware transformers – start-page: 1 year: 2010 ident: 10.1016/j.cag.2024.104104_b12 article-title: Destroying LA for ”2012” – volume: 41 start-page: 177:1 issue: 5 year: 2022 ident: 10.1016/j.cag.2024.104104_b7 article-title: Simulating brittle fracture with material points publication-title: ACM Trans Graph doi: 10.1145/3522573 – start-page: 7 year: 2015 ident: 10.1016/j.cag.2024.104104_b14 article-title: Automatic 3D object fracturing for evaluation of partial retrieval and object restoration tasks - benchmark and application to 3D cultural heritage data – volume: 113 start-page: 102 year: 2023 ident: 10.1016/j.cag.2024.104104_b20 article-title: TAssembly: Data-driven fractured object assembly using a linear template model publication-title: Comput Graph doi: 10.1016/j.cag.2023.05.003 – volume: 21 start-page: 291 issue: 3 year: 2002 ident: 10.1016/j.cag.2024.104104_b4 article-title: Graphical modeling and animation of ductile fracture publication-title: ACM Trans Graph doi: 10.1145/566654.566579 – volume: 27 start-page: 3733 issue: 9 year: 2021 ident: 10.1016/j.cag.2024.104104_b40 article-title: QuadStack: An efficient representation and direct rendering of layered datasets publication-title: IEEE Trans Vis Comput Graphics doi: 10.1109/TVCG.2020.2981565 – ident: 10.1016/j.cag.2024.104104_b31 doi: 10.1109/ICCV.2019.00939 – ident: 10.1016/j.cag.2024.104104_b28 – ident: 10.1016/j.cag.2024.104104_b41 – start-page: 1 year: 2021 ident: 10.1016/j.cag.2024.104104_b15 article-title: OPENVDB – volume: 41 start-page: 65 issue: 5 year: 2022 ident: 10.1016/j.cag.2024.104104_b13 article-title: MendNet: Restoration of fractured shapes using learned occupancy functions publication-title: Comput Graph Forum doi: 10.1111/cgf.14603 – year: 2018 ident: 10.1016/j.cag.2024.104104_b32 – year: 2021 ident: 10.1016/j.cag.2024.104104_b25 – volume: 8 start-page: 12675 year: 2020 ident: 10.1016/j.cag.2024.104104_b35 article-title: A GPU-based framework for generating implicit datasets of voxelized polygonal models for the training of 3D convolutional neural networks publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2965624 – start-page: 209 year: 1997 ident: 10.1016/j.cag.2024.104104_b38 article-title: Surface simplification using quadric error metrics – year: 2022 ident: 10.1016/j.cag.2024.104104_b17 – volume: 8 start-page: 343 issue: 3 year: 2009 ident: 10.1016/j.cag.2024.104104_b2 article-title: A fast robust algorithm for computing discrete voronoi diagrams publication-title: J Math Model Algorithms doi: 10.1007/s10852-008-9097-6 – ident: 10.1016/j.cag.2024.104104_b27 doi: 10.1109/CVPR52729.2023.00433 – start-page: 283 year: 2020 ident: 10.1016/j.cag.2024.104104_b18 article-title: Weakly-supervised 3D shape completion in the wild – volume: 76 start-page: 21565 issue: 20 year: 2017 ident: 10.1016/j.cag.2024.104104_b3 article-title: Classification of archaeological pottery profiles using modal analysis publication-title: Multimedia Tools Appl doi: 10.1007/s11042-016-4076-9 – ident: 10.1016/j.cag.2024.104104_b26 doi: 10.1109/ICCV48922.2021.00644 – volume: 23 year: 2012 ident: 10.1016/j.cag.2024.104104_b11 article-title: Practical simulation of hierarchical brittle fracture publication-title: Comput. Animat. Virtual Worlds doi: 10.1002/cav.1443 – volume: 100 start-page: 61 year: 2018 ident: 10.1016/j.cag.2024.104104_b36 article-title: Efficient voxelization using projected optimal scanline publication-title: Graph Models doi: 10.1016/j.gmod.2017.06.004 – start-page: 11164 year: 2020 ident: 10.1016/j.cag.2024.104104_b30 article-title: LiDARsim: Realistic LiDAR simulation by leveraging the real world – volume: 42 start-page: 10:1 issue: 1 year: 2023 ident: 10.1016/j.cag.2024.104104_b34 article-title: Breaking good: Fracture modes for realtime destruction publication-title: ACM Trans Graph doi: 10.1145/3549540 |
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