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We present Surf-D, a novel method for generating high-quality 3D shapes as Surfaces with arbitrary topologies using Diffusion models.
Canny, J.: A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence (6), 679–698 (1986)
1986
Earlier work this paper cites.
Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th annual international conference on machine learning. pp. 41–48 (2009)
2009
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International conference on machine learning. pp. 2256–2265. PMLR (2015)
2015
Earlier work this paper cites.
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: A deep representation for volumetric shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1912–1920 (2015)
2015
Earlier work this paper cites.
Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. Advances in neural information processing systems 29
2016
Earlier work this paper cites.
De Vries, H., Strub, F., Mary, J., Larochelle, H., Pietquin, O., Courville, A.C.: Modulating early visual processing by language. Advances in Neural Information Processing Systems 30
2017
Earlier work this paper cites.
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)
2017
Earlier work this paper cites.
Smith, E.J., Meger, D.: Improved adversarial systems for 3d object generation and reconstruction (2017)
2017
Earlier work this paper cites.
Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.: Learning representations and generative models for 3d point clouds. In: ICML (2018)
2018
Earlier work this paper cites.
Chen, K., Choy, C.B., Savva, M., Chang, A.X., Funkhouser, T., Savarese, S.: Text2shape: Generating shapes from natural language by learning joint embeddings. In: ACCV (2018)
2018
Earlier work this paper cites.
Sun, X., Wu, J., Zhang, X., Zhang, Z., Zhang, C., Xue, T., Tenenbaum, J.B., Freeman, W.T.: Pix3d: Dataset and methods for single-image 3d shape modeling. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2974–2983 (2018)
2018
Earlier work this paper cites.
Xie, J., Zheng, Z., Gao, R., Wang, W., Zhu, S.C., Wu, Y.N.: Learning descriptor networks for 3d shape synthesis and analysis. In: CVPR (2018)
2018
Earlier work this paper cites.
Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: CVPR (2019)
2019
Earlier work this paper cites.
Gao, L., Yang, J., Wu, T., Yuan, Y.J., Fu, H., Lai, Y.K., Zhang, H.: Sdm-net: Deep generative network for structured deformable mesh. ACM Transactions on Graphics (TOG) 38
2019
Earlier work this paper cites.
Mahmood, N., Ghorbani, N., Troje, N.F., Pons-Moll, G., Black, M.J.: Amass: Archive of motion capture as surface shapes. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 5442–5451 (2019)
2019
Earlier work this paper cites.
Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: Deepsdf: Learning continuous signed distance functions for shape representation. In: CVPR (2019)
2019
Earlier work this paper cites.
Wang, T.Y., Shao, T., Fu, K., Mitra, N.J.: Learning an intrinsic garment space for interactive authoring of garment animation. ACM Transactions on Graphics (TOG) 38
2019
Earlier work this paper cites.
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics (tog) 38
2019
Earlier work this paper cites.
Xu, Q., Wang, W., Ceylan, D., Mech, R., Neumann, U.: Disn: Deep implicit surface network for high-quality single-view 3d reconstruction. In: NeurIPS (2019)
2019
Earlier work this paper cites.
Yang, G., Huang, X., Hao, Z., Liu, M.Y., Belongie, S., Hariharan, B.: Pointflow: 3d point cloud generation with continuous normalizing flows. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 4541–4550 (2019)
2019
Earlier work this paper cites.
Duan, Y., Zhu, H., Wang, H., Yi, L., Nevatia, R., Guibas, L.J.: Curriculum deepsdf. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VIII 16. pp. 51–67. Springer (2020)
2020
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. In: ECCV (2020)
2020
Earlier work this paper cites.
Patel, C., Liao, Z., Pons-Moll, G.: Tailornet: Predicting clothing in 3d as a function of human pose, shape and garment style. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 7365–7375 (2020)
2020
Earlier work this paper cites.
Zhu, H., Cao, Y., Jin, H., Chen, W., Du, D., Wang, Z., Cui, S., Han, X.: Deep fashion3d: A dataset and benchmark for 3d garment reconstruction from single images. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16. pp. 512–530. Springer (2020)
2020
Earlier work this paper cites.
Chan, E.R., Monteiro, M., Kellnhofer, P., Wu, J., Wetzstein, G.: pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis. In: CVPR (2021)
2021
Earlier work this paper cites.
Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in neural information processing systems 34
2021
Earlier work this paper cites.
Luo, S., Hu, W.: Diffusion probabilistic models for 3d point cloud generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2837–2845 (2021)
2021
Earlier work this paper cites.
Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning. pp. 8162–8171. PMLR (2021)
2021
Earlier work this paper cites.
Peng, S., Jiang, C., Liao, Y., Niemeyer, M., Pollefeys, M., Geiger, A.: Shape as points: A differentiable poisson solver. Advances in Neural Information Processing Systems 34
2021
Earlier work this paper cites.
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)
2021
Earlier work this paper cites.
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
2021
Cited alongside, same era.
Sun, J., Xie, Y., Chen, L., Zhou, X., Bao, H.: Neuralrecon: Real-time coherent 3d reconstruction from monocular video. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15598–15607 (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Zhou, L., Du, Y., Wu, J.: 3d shape generation and completion through point-voxel diffusion. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5826–5835 (2021)
2021
Cited alongside, same era.
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
Closest in time.
Li, Y., Dou, Y., Chen, X., Ni, B., Sun, Y., Liu, Y., Wang, F.: Generalized deep 3d shape prior via part-discretized diffusion process. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 16784–16794 (June 2023)
2023
Closest in time.
Li, Z., Müller, T., Evans, A., Taylor, R.H., Unberath, M., Liu, M.Y., Lin, C.H.: Neuralangelo: High-fidelity neural surface reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8456–8465 (2023)
2023
Closest in time.
Liu, M., Xu, C., Jin, H., Chen, L., T, M.V., Xu, Z., Su, H.: One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization (2023)
2023
Closest in time.
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Chan, E.R., Lin, C.Z., Chan, M.A., Nagano, K., Pan, B., De Mello, S., Gallo, O., Guibas, L.J., Tremblay, J., Khamis, S., et al.: Efficient geometry-aware 3d generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16123–16133 (2022)
2022
Cited alongside, same era.
Chen, W., Lin, C., Li, W., Yang, B.: 3psdf: Three-pole signed distance function for learning surfaces with arbitrary topologies. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18522–18531 (2022)
2022
Cited alongside, same era.
Fu, R., Zhan, X., Chen, Y., Ritchie, D., Sridhar, S.: Shapecrafter: A recursive text-conditioned 3d shape generation model. Advances in Neural Information Processing Systems 35
2022
Cited alongside, same era.
Gu, J., Liu, L., Wang, P., Theobalt, C.: Stylenerf: A style-based 3d-aware generator for high-resolution image synthesis. In: ICLR (2022)
2022
Cited alongside, same era.
Guillard, B., Stella, F., Fua, P.: Meshudf: Fast and differentiable meshing of unsigned distance field networks. In: European Conference on Computer Vision. pp. 576–592. Springer (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Hui, K.H., Li, R., Hu, J., Fu, C.W.: Neural wavelet-domain diffusion for 3d shape generation. In: SIGGRAPH Asia 2022 Conference Papers. pp. 1–9 (2022)
2022
Cited alongside, same era.
Liu, Z., Wang, Y., Qi, X., Fu, C.W.: Towards implicit text-guided 3d shape generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 17896–17906 (2022)
2022
Cited alongside, same era.
Liu, R., Wu, R., Van Hoorick, B., Tokmakov, P., Zakharov, S., Vondrick, C.: Zero-1-to-3: Zero-shot one image to 3d object. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9298–9309 (2023)
2023
Closest in time.
Liu, Y.T., Wang, L., Yang, J., Chen, W., Meng, X., Yang, B., Gao, L.: Neudf: Leaning neural unsigned distance fields with volume rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 237–247 (2023)
2023
Closest in time.
2023
Closest in time.
Liu, Z., Feng, Y., Black, M.J., Nowrouzezahrai, D., Paull, L., Liu, W.: Meshdiffusion: Score-based generative 3d mesh modeling. In: The Eleventh International Conference on Learning Representations (2023), https://openreview.net/forum?id=0cpM2ApF9p6
2023
Closest in time.
Liu, Z., Feng, Y., Xiu, Y., Liu, W., Paull, L., Black, M.J., Schölkopf, B.: Ghost on the shell: An expressive representation of general 3d shapes (2023)
2023
Closest in time.
Liu, Z., Dai, P., Li, R., Qi, X., Fu, C.W.: Dreamstone: Image as stepping stone for text-guided 3d shape generation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Long, X., Lin, C., Liu, L., Liu, Y., Wang, P., Theobalt, C., Komura, T., Wang, W.: Neuraludf: Learning unsigned distance fields for multi-view reconstruction of surfaces with arbitrary topologies. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20834–20843 (2023)
2023
Closest in time.
Lyu, Z., Wang, J., An, Y., Zhang, Y., Lin, D., Dai, B.: Controllable mesh generation through sparse latent point diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 271–280 (2023)
2023
Closest in time.
Meng, X., Chen, W., Yang, B.: Neat: Learning neural implicit surfaces with arbitrary topologies from multi-view images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 248–258 (2023)
2023
Closest in time.
Qi, Z., Yu, M., Dong, R., Ma, K.: VPP: Efficient universal 3d generation via voxel-point progressive representation. In: Thirty-seventh Conference on Neural Information Processing Systems (2023), https://openreview.net/forum?id=etd0ebzGOG
2023
Closest in time.
Sanghi, A., Fu, R., Liu, V., Willis, K.D., Shayani, H., Khasahmadi, A.H., Sridhar, S., Ritchie, D.: Clip-sculptor: Zero-shot generation of high-fidelity and diverse shapes from natural language. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18339–18348 (2023)
2023
Closest in time.
Shen, T., Munkberg, J., Hasselgren, J., Yin, K., Wang, Z., Chen, W., Gojcic, Z., Fidler, S., Sharp, N., Gao, J.: Flexible isosurface extraction for gradient-based mesh optimization. ACM Trans. Graph. 42
2023
Closest in time.
2023
Closest in time.
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)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Wu, T., Gao, L., Zhang, L.X., Lai, Y.K., Zhang, H.: Star-tm: Structure aware reconstruction of textured mesh from single image. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)
2023
Closest in time.
Wu, Z., Wang, Y., Feng, M., Xie, H., Mian, A.: Sketch and text guided diffusion model for colored point cloud generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 8929–8939 (2023)
2023
Closest in time.
2023
Closest in time.
Zheng, X.Y., Pan, H., Wang, P.S., Tong, X., Liu, Y., Shum, H.Y.: Locally attentional sdf diffusion for controllable 3d shape generation. ACM Transactions on Graphics (SIGGRAPH) 42
2023
Closest in time.
2023
Closest in time.
2024
Closest in time.
2024
Closest in time.
Liao, Z., Wang, S., Komura, T.: Senc: Handling self-collision in neural cloth simulation. In: European Conference on Computer Vision (2024)
2024
Closest in time.