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This paper presents a new approach for 3D shape generation, enabling direct generative modeling on a continuous implicit representation in wavelet domain.
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DeepSDF: Learning continuous signed distance functions for shape representation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 165–174
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A skeleton-bridged deep learning approach for generating meshes of complex topologies from single RGB images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 4541–4550
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Multiresolution Deep Implicit Functions for 3D Shape Representation. In IEEE International Conference on Computer Vision (ICCV) . 13087–13096
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Diffusion models beat GANS on image synthesis
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3D Shape Generation With Grid-Based Implicit Functions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 13559–13568
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D 2 IM-Net: Learning detail disentangled implicit fields from single images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 10246–10255
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DISN: Deep implicit surface network for high-quality single-view 3D reconstruction. In Conference on Neural Information Processing Systems (NeurIPS) . 490–500
Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann. 2019 · 2019
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PointFlow: 3D point cloud generation with continuous normalizing flows. In IEEE International Conference on Computer Vision (ICCV) . 4541–4550
Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan. 2019 · 2019
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SAL: Sign agnostic learning of shapes from raw data. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 2565–2574
Matan Atzmon and Yaron Lipman. 2020 · 2020
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Learning gradient fields for shape generation. In European Conference on Computer Vision (ECCV) . 364–381
Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor, Zekun Hao, Serge Belongie, Noah Snavely, and Bharath Hariharan. 2020 · 2020
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Implicit functions in feature space for 3D shape reconstruction and completion. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 6970–6981
Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll. 2020 · 2020
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Uses of Complex Wavelets in Deep Convolutional Neural Networks
Fergal Cotter. 2020 · 2020
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Multiplicative filter networks. In International Conference on Learning Representations (ICLR)
Rizal Fathony, Anit Kumar Sahu, Devin Willmott, and J. Zico Kolter. 2020 · 2020
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Implicit geometric regularization for learning shapes. In Proceedings of International Conference on Machine Learning (ICML) . 3569–3579
Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman. 2020 · 2020
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Manyi Li and Hao Zhang. 2021 · 2021
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SP-GAN:Sphere-Guided 3D Shape Generation and Manipulation
Ruihui Li, Xianzhi Li, Ke-Hei Hui, and Chi-Wing Fu. 2021 · 2021
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Deep Implicit Moving Least-Squares Functions for 3D Reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 1788–1797
Shi-Lin Liu, Hao-Xiang Guo, Hao Pan, Pengshuai Wang, Xin Tong, and Yang Liu. 2021 · 2021
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SurfGen: Adversarial 3D Shape Synthesis with Explicit Surface Discriminators. In IEEE International Conference on Computer Vision (ICCV) . 16238–16248
Andrew Luo, Tianqin Li, Wen-Hao Zhang, and Tai Sing Lee. 2021 · 2021
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Diffusion probabilistic models for 3D point cloud generation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 2837–2845
Shitong Luo and Wei Hu. 2021 · 2021
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ACORN: Adaptive coordinate networks for neural scene representation
Julien N. P. Martel, David B. Lindell, Connor Z. Lin, Eric R. Chan, Marco Monteiro, and Gordon Wetzstein. 2021 · 2021
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Improved denoising diffusion probabilistic models. In Proceedings of International Conference on Machine Learning (ICML) . 8162–8171
Alexander Quinn Nichol and Prafulla Dhariwal. 2021 · 2021
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Neural geometric level of detail: Real-time rendering with implicit 3D shapes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 11358–11367
Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler. 2021 · 2021
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SkeletonNet: A topology-preserving solution for learning mesh reconstruction of object surfaces from RGB images
Jiapeng Tang, Xiaoguang Han, Mingkui Tan, Xin Tong, and Kui Jia. 2021 · 2021
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Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction from Raw Point Clouds. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 10256–10265
Wenbin Zhao, Jiabao Lei, Yuxin Wen, Jianguo Zhang, and Kui Jia. 2021 · 2021
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3D shape generation and completion through point-voxel diffusion. In IEEE International Conference on Computer Vision (ICCV) . 5826–5835
Linqi Zhou, Yilun Du, and Jiajun Wu. 2021 · 2021
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SPAGHETTI: Editing Implicit Shapes Through Part Aware Generation
Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung, and Daniel Cohen-Or. 2022 · 2022
Closest in time.
MINER: Multiscale Implicit Neural Representations
Vishwanath Saragadam, Jasper Tan, Guha Balakrishnan, Richard G. Baraniuk, and Ashok Veeraraghavan. 2022 · 2022
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Shapeformer: Transformer-based shape completion via sparse representation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 6239–6249
Xingguang Yan, Liqiang Lin, Niloy J Mitra, Dani Lischinski, Daniel Cohen-Or, and Hui Huang. 2022 · 2022
Closest in time.
SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation. In Eurographics Symposium on Geometry Processing (SGP)
Xin-Yang Zheng, Yang Liu, Peng-Shuai Wang, and Xin Tong. 2022 · 2022
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MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement. In IEEE International Conference on Computer Vision (ICCV) . 2039–2048
Rinon Gal, Amit Bermano, Hao Zhang, and Daniel Cohen-Or. 2020 · 2048
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