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3D generation has witnessed significant advancements, yet efficiently producing high-quality 3D assets from a single image remains challenging.
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Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 652–660 (2017)
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
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Nash, C., Ganin, Y., Eslami, S.A., Battaglia, P.: Polygen: An autoregressive generative model of 3d meshes. In: International conference on machine learning. pp. 7220–7229. PMLR (2020)
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Li, R., Li, X., Hui, K.H., Fu, C.W.: Sp-gan: Sphere-guided 3d shape generation and manipulation. ACM Transactions on Graphics (TOG) 40
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Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM 65
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
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Shen, T., Gao, J., Yin, K., Liu, M.Y., Fidler, S.: Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis. Advances in Neural Information Processing Systems 34
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Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)
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Mittal, P., Cheng, Y.C., Singh, M., Tulsiani, S.: Autosdf: Shape priors for 3d completion, reconstruction and generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 306–315 (2022)
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Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al.: Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems 35
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Zhang, B., Nießner, M., Wonka, P.: 3dilg: Irregular latent grids for 3d generative modeling. Advances in Neural Information Processing Systems 35
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2023
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Cheng, Y.C., Lee, H.Y., Tulyakov, S., Schwing, A.G., Gui, L.Y.: Sdfusion: Multimodal 3d shape completion, reconstruction, and generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4456–4465 (2023)
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Deitke, M., Schwenk, D., Salvador, J., Weihs, L., Michel, O., VanderBilt, E., Schmidt, L., Ehsani, K., Kembhavi, A., Farhadi, A.: Objaverse: A universe of annotated 3d objects. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13142–13153 (2023)
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Shim, J., Kang, C., Joo, K.: Diffusion-based signed distance fields for 3d shape generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20887–20897 (2023)
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Shue, J.R., Chan, E.R., Po, R., Ankner, Z., Wu, J., Wetzstein, G.: 3d neural field generation using triplane diffusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20875–20886 (2023)
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2023
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He, Z., Wang, T.: Openlrm: Open-source large reconstruction models. https://github.com/3DTopia/OpenLRM (2023)
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2023
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2023
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Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics 42
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Li, M., Duan, Y., Zhou, J., Lu, J.: Diffusion-sdf: Text-to-shape via voxelized diffusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12642–12651 (2023)
2023
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Li, S., Li, C., Zhu, W., Yu, B., Zhao, Y., Wan, C., You, H., Shi, H., Lin, Y.: Instant-3d: Instant neural radiance field training towards on-device ar/vr 3d reconstruction. In: Proceedings of the 50th Annual International Symposium on Computer Architecture. pp. 1–13 (2023)
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Lin, C.H., Gao, J., Tang, L., Takikawa, T., Zeng, X., Huang, X., Kreis, K., Fidler, S., Liu, M.Y., Lin, T.Y.: Magic3d: High-resolution text-to-3d content creation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 300–309 (2023)
2023
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2023
Later among the works it cites.
Wang, T., Zhang, B., Zhang, T., Gu, S., Bao, J., Baltrusaitis, T., Shen, J., Chen, D., Wen, F., Chen, Q., et al.: Rodin: A generative model for sculpting 3d digital avatars using diffusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4563–4573 (2023)
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Wu, L., Wang, D., Gong, C., Liu, X., Xiong, Y., Ranjan, R., Krishnamoorthi, R., Chandra, V., Liu, Q.: Fast point cloud generation with straight flows. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9445–9454 (2023)
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2023
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2023
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2023
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Deitke, M., Liu, R., Wallingford, M., Ngo, H., Michel, O., Kusupati, A., Fan, A., Laforte, C., Voleti, V., Gadre, S.Y., et al.: Objaverse-xl: A universe of 10m+ 3d objects. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Liu, M., Shi, R., Kuang, K., Zhu, Y., Li, X., Han, S., Cai, H., Porikli, F., Su, H.: Openshape: Scaling up 3d shape representation towards open-world understanding. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Liu, M., Xu, C., Jin, H., Chen, L., Varma T, M., Xu, Z., Su, H.: One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization. Advances in Neural Information Processing Systems 36
2024
Closest in time.
2024
Closest in time.