Distribution-motivated 3D Style Characterization Based on Latent Feature Decomposition

Computer-Aided Design - Tập 153 - Trang 103399 - 2022
Xinwei Huang1, Shuai Li1, Shoulong Zhang1, Aimin Hao1, Hong Qin2
1State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, 100191, Beijing, China
2Department of Computer Science, Stony Brook University, 11794-2424, NY, USA

Tài liệu tham khảo

Hu, 2017, Co-locating style-defining elements on 3d shapes, ACM Trans Graph, 36, 1, 10.1145/3092817 Liu, 2015, Style compatibility for 3D furniture models, ACM Trans Graph, 34, 1, 10.1145/2766898 Lun, 2015, Elements of style: learning perceptual shape style similarity, ACM Trans Graph, 34, 1, 10.1145/2766929 Xu K, Li H, Zhang H, Cohen-Or D, Xiong Y, Cheng ZQ. Style-content separation by anisotropic part scales. In: ACM SIGGRAPH asia 2010 papers. 2010, p. 1–10. Liu, 2019 Lun, 2016, Functionality preserving shape style transfer, ACM Trans Graph, 35, 1, 10.1145/2980179.2980237 Chen Z, Kim VG, Fisher M, Aigerman N, Zhang H, Chaudhuri S. DECOR-GAN: 3D shape detailization by conditional refinement. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021, p. 15740–9. Ma, 2014, Analogy-driven 3D style transfer, Comput Graph Forum, 33, 175, 10.1111/cgf.12307 Yin, 2021 Segu, 2020 Anguelov D, Srinivasan P, Koller D, Thrun S, Rodgers J, Davis J. SCAPE: shape completion and animation of people. In: ACM SIGGRAPH 2005 papers. 2005, p. 408–16. Sumner, 2004, Deformation transfer for triangle meshes, ACM Trans Graph, 23, 399, 10.1145/1015706.1015736 Wang W, Ceylan D, Mech R, Neumann U. 3dn: 3d deformation network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2019, p. 1038–46. Gao, 2019, SDM-NET: Deep generative network for structured deformable mesh, ACM Trans Graph, 38, 1, 10.1145/3355089.3356488 Tan Q, Gao L, Lai YK, Xia S. Variational autoencoders for deforming 3d mesh models. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, p. 5841–50. Yifan W, Aigerman N, Kim VG, Chaudhuri S, Sorkine-Hornung O. Neural cages for detail-preserving 3D deformations. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2020, p. 75–83. Lun, 2017, 3D shape reconstruction from sketches via multi-view convolutional networks, 67 Wang, 2018, Global-to-local generative model for 3d shapes, ACM Trans Graph, 37, 1 Li J, Niu C, Xu K. Learning part generation and assembly for structure-aware shape synthesis. In: Proceedings of the AAAI conference on artificial intelligence. 34, (07):2020, p. 11362–9. Wang, 2017, O-cnn: Octree-based convolutional neural networks for 3d shape analysis, ACM Trans Graph, 36, 1 Qi CR, Su H, Mo K, Guibas LJ. Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, p. 652–60. Mo, 2019 Huang, 2020 Chen Z, Zhang H. Learning implicit fields for generative shape modeling. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2019, p. 5939–48. Arsalan Soltani A, Huang H, Wu J, Kulkarni TD, Tenenbaum JB. Synthesizing 3d shapes via modeling multi-view depth maps and silhouettes with deep generative networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, p. 1511–9. Choy, 2016, 3D-r2n2: A unified approach for single and multi-view 3d object reconstruction, 628 Gadelha M, Maji S, Wang R. 3D shape generation using spatially ordered point clouds. In: British machine vision conference. 3, 2017. Hane, 2017, Hierarchical surface prediction for 3d object reconstruction, 412 Richter SR, Roth S. Matryoshka networks: Predicting 3d geometry via nested shape layers. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, p. 1936–44. Tatarchenko M, Dosovitskiy A, Brox T. Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs. In: Proceedings of the IEEE international conference on computer vision. 2017, p. 2088–96. Qi, 2017, Pointnet++: Deep hierarchical feature learning on point sets in a metric space, 5099 Achlioptas, 2018, Learning representations and generative models for 3d point clouds, 40 Fan H, Su H, Guibas LJ. A point set generation network for 3d object reconstruction from a single image. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, p. 605–13. Gadelha M, Wang R, Maji S. Multiresolution tree networks for 3d point cloud processing. In: Proceedings of the european conference on computer vision. 2018, p. 103–18. Yang Y, Feng C, Shen Y, Tian D. FoldingNet: point cloud auto-encoder via deep grid deformation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. Groueix T, Fisher M, Kim VG, Russell BC, Aubry M. A Papier-mâché Approach to Learning 3D Surface Generation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. Kanazawa A, Tulsiani S, Efros AA, Malik J. Learning category-specific mesh reconstruction from image collections. In: Proceedings of the european conference on computer vision. 2018, p. 371–86. Wang N, Zhang Y, Li Z, Fu Y, Liu W, Jiang YG. Pixel2mesh: Generating 3d mesh models from single rgb images. In: Proceedings of the european conference on computer vision. 2018, p. 52–67. Chen Z, Tagliasacchi A, Zhang H. Bsp-net: Generating compact meshes via binary space partitioning. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2020, p. 45–54. Genova K, Cole F, Vlasic D, Sarna A, Freeman WT, Funkhouser T. Learning shape templates with structured implicit functions. In: Proceedings of the IEEE international conference on computer vision. 2019, p. 7154–64. Mescheder L, Oechsle M, Niemeyer M, Nowozin S, Geiger A. Occupancy networks: Learning 3d reconstruction in function space. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2019, p. 4460–70. Park JJ, Florence P, Straub J, Newcombe R, Lovegrove S. Deepsdf: Learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2019, p. 165–74. Gatys LA, Ecker AS, Bethge M. Image style transfer using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, p. 2414–23. Li, 2017 Rubner, 2000, The earth moverś distance as a metric for image retrieval, Int J Comput Vis, 40, 99, 10.1023/A:1026543900054 Maturana, 2015, Voxnet: A 3d convolutional neural network for real-time object recognition, 922 Chang, 2015