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Incomplete or missing data in three-dimensional (3D) models can lead to erroneous or flawed renderings, limiting their usefulness in applications such as visualization, geometric computation, and 3D printing.
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Image inpainting
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J. Davis, S. R. Marschner, M. Garr, and M. Levoy · 2002
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A. Criminisi, P. Pérez, and K. Toyama · 2004
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Context-based surface completion
A. Sharf, M. Alexa, and D. Cohen-Or · 2004
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Filling holes in complex surfaces using oriented voxel diffusion
T.-Q. Guo, J.-J. Li, J.-G. Weng, and Y.-T. Zhuang · 2006
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L. Shapira, A. Shamir, and D. Cohen-Or · 2008
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X. J. Wu, M. Y. Wang, and B. Han · 2008
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M. Attene · 2010
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Polygon mesh processing
M. Botsch, L. Kobbelt, M. Pauly, P. Alliez, and B. Lévy · 2010
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A. Bugeau, M. Bertalmío, V. Caselles, and G. Sapiro · 2010
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Image quality metrics: Psnr vs. ssim
A. Hore and D. Ziou · 2010
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Hole filling based on local surface approximation
H. Qiang, Z. Shusheng, B. Xiaoliang, and Z. Xin · 2010
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M. Wei, J. Wu, and M. Pang · 2010
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Are we ready for autonomous driving? the kitti vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
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Screened poisson surface reconstruction
M. Kazhdan and H. Hoppe · 2013
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M. Centin and A. Signoroni · 2015
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Fully convolutional networks for semantic segmentation
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U-net: Convolutional networks for biomedical image segmentation
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3d shapenets: A deep representation for volumetric shapes
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Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections
X. Mao, C. Shen, and Y.-B. Yang · 2016
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Context encoders: Feature learning by inpainting
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros · 2016
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner · 2017
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Shape completion using 3d-encoder-predictor cnns and shape synthesis
A. Dai, C. Ruizhongtai Qi, and M. Nießner · 2017
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A point set generation network for 3d object reconstruction from a single image
Point cloud completion by skip-attention network with hierarchical folding
X. Wen, T. Li, Z. Han, and Y.-S. Liu · 2020
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Unsupervised shape completion via deep prior in the neural tangent kernel perspective
L. Chu, H. Pan, and W. Wang · 2021
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Transgan: Two transformers can make one strong gan
Y. Jiang, S. Chang, and Z. Wang · 2021
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Repairing 3d models obtained from range sensors
E. Pérez, S. Salamanca, P. Merchán, and A. Adán · 2021
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Learning transferable visual models from natural language supervision
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H. Fan, H. Su, and L. J. Guibas · 2017
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Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. Osman Ulusoy, and A. Geiger · 2017
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Joint gap detection and inpainting of line drawings
K. Sasaki, S. Iizuka, E. Simo-Serra, and H. Ishikawa · 2017
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Demeshnet: Blind face inpainting for deep meshface verification
S. Zhang, R. He, Z. Sun, and T. Tan · 2017
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Places: A 10 million image database for scene recognition
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba · 2017
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Perceptually aware image inpainting
D. Ding, S. Ram, and J. J. Rodriguez · 2018
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3d reconstruction of incomplete archaeological objects using a generative adversarial network
R. Hermoza and I. Sipiran · 2018
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R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 2021
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Resolution-robust large mask inpainting with fourier convolutions
R. Suvorov, E. Logacheva, A. Mashikhin, A. Remizova, A. Ashukha, A. Silvestrov, N. Kong, H. Goka, K. Park, and V. Lempitsky · 2021
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Frechet inception distance (fid) for evaluating gans
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Cr-fill: Generative image inpainting with auxiliary contextual reconstructionyu2018generative
Y. Zeng, Z. Lin, H. Lu, and V. M. Patel · 2021
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Unsupervised 3d shape completion through gan inversion
J. Zhang, X. Chen, Z. Cai, L. Pan, H. Zhao, S. Yi, C. K. Yeo, B. Dai, and C. C. Loy · 2021
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Large scale image completion via co-modulated generative adversarial networks
S. Zhao, J. Cui, Y. Sheng, Y. Dong, X. Liang, E. I. Chang, and Y. Xu · 2021
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Image inpainting by end-to-end cascaded refinement with mask awareness
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Abo: Dataset and benchmarks for real-world 3d object understanding
J. Collins, S. Goel, K. Deng, A. Luthra, L. Xu, E. Gundogdu, X. Zhang, T. F. Y. Vicente, T. Dideriksen, H. Arora, et al · 2022
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Google scanned objects: A high-quality dataset of 3d scanned household items
L. Downs, A. Francis, N. Koenig, B. Kinman, R. Hickman, K. Reymann, T. B. McHugh, and V. Vanhoucke · 2022
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Learning self-prior for mesh denoising using dual graph convolutional networks
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Mat: Mask-aware transformer for large hole image inpainting
W. Li, Z. Lin, K. Zhou, L. Qi, Y. Wang, and J. Jia · 2022
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Dreamfusion: Text-to-3d using 2d diffusion
B. Poole, A. Jain, J. T. Barron, and B. Mildenhall · 2022
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Hierarchical text-conditional image generation with clip latents
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen · 2022
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Data-driven restoration of digital archaeological pottery with point cloud analysis
I. Sipiran, A. Mendoza, A. Apaza, and C. Lopez · 2022
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Aggregated contextual transformations for high-resolution image inpainting
Y. Zeng, J. Fu, H. Chao, and B. Guo · 2022
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Bridging global context interactions for high-fidelity image completion
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Objaverse: A universe of annotated 3d objects
M. Deitke, D. Schwenk, J. Salvador, L. Weihs, O. Michel, E. VanderBilt, L. Schmidt, K. Ehsani, A. Kembhavi, and A. Farhadi · 2023
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Inpainting holes in folded fabric meshes
G. Gisbert, R. Chaine, and D. Coeurjolly · 2023
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Deep learning of curvature features for shape completion
M. Hernández-Bautista and F. Melero · 2023
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Texture inpainting for photogrammetric models
A. Maggiordomo, P. Cignoni, and M. Tarini · 2023
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Reference-guided controllable inpainting of neural radiance fields
A. Mirzaei, T. Aumentado-Armstrong, M. A. Brubaker, J. Kelly, A. Levinshtein, K. G. Derpanis, and I. Gilitschenski · 2023
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Unsupervised point cloud representation learning with deep neural networks: A survey
A. Xiao, J. Huang, D. Guan, X. Zhang, S. Lu, and L. Shao · 2023
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Learning self-prior for mesh inpainting using self-supervised graph convolutional networks
S. Hattori, T. Yatagawa, Y. Ohtake, and H. Suzuki · 2024
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