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Reconstructing and simulating elastic objects from visual observations is crucial for applications in computer vision and robotics.
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Hu, Y., Fang, Y., Ge, Z., Qu, Z., Zhu, Y., Pradhana, A., Jiang, C.: A moving least squares material point method with displacement discontinuity and two-way rigid body coupling. ACM TOG 37
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Raissi, M., Perdikaris, P., Karniadakis, G.: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics 378
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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. In: ECCV (2020)
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Sanchez-Gonzalez, A., Godwin, J., Pfaff, T., Ying, R., Leskovec, J., Battaglia, P.W.: Learning to simulate complex physics with graph networks. In: ICML (2020)
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Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., Srinivasan, P.P.: Mip-NeRF: A multiscale representation for anti-aliasing neural radiance fields. In: CVPR (2021)
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Chen, A., Xu, Z., Zhao, F., Zhang, X., Xiang, F., Yu, J., Su, H.: Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In: ICCV (2021)
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Pumarola, A., Corona, E., Pons-Moll, G., Moreno-Noguer, F.: D-NeRF: Neural Radiance Fields for Dynamic Scenes. In: CVPR (2021)
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Tretschk, E., Tewari, A., Golyanik, V., Zollhöfer, M., Lassner, C., Theobalt, C.: Non-rigid neural radiance fields: Reconstruction and novel view synthesis of a dynamic scene from monocular video. In: ICCV (2021)
2021
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Le Cleac’h, S., Yu, H.X., Guo, M., Howell, T., Gao, R., Wu, J., Manchester, Z., Schwager, M.: Differentiable physics simulation of dynamics-augmented neural objects. IEEE Robotics and Automation Letters 8
2023
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Li, X., Qiao, Y.L., Chen, P.Y., Jatavallabhula, K.M., Lin, M., Jiang, C., Gan, C.: PAC-NeRF: Physics augmented continuum neural radiance fields for geometry-agnostic system identification. In: ICLR (2023)
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2021
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Chu, M., Liu, L., Zheng, Q., Franz, E., Seidel, H.P., Theobalt, C., Zayer, R.: Physics informed neural fields for smoke reconstruction with sparse data. ACM TOG 41
2022
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Guan, S., Deng, H., Wang, Y., Yang, X.: Neurofluid: Fluid dynamics grounding with particle-driven neural radiance fields. In: ICML (2022)
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Li, Y., Li, S., Sitzmann, V., Agrawal, P., Torralba, A.: 3d neural scene representations for visuomotor control. In: Conference on Robot Learning. pp. 112–123. PMLR (2022)
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Liu, Y., Peng, S., Liu, L., Wang, Q., Wang, P., Christian, T., Zhou, X., Wang, W.: Neural rays for occlusion-aware image-based rendering. In: CVPR (2022)
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Chen, H., Wu, R., Grinspun, E., Zheng, C., Chen, P.Y.: Implicit neural spatial representations for time-dependent pdes. In: ICML (2023)
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2023
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Ma, P., Chen, P.Y., Deng, B., Tenenbaum, J.B., Du, T., Gan, C., Matusik, W.: Learning neural constitutive laws from motion observations for generalizable pde dynamics. In: ICML (2023)
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Yu, H.X., Zheng, Y., Gao, Y., Deng, Y., Zhu, B., Wu, J.: Inferring hybrid neural fluid fields from videos. Advances in Neural Information Processing Systems (2023)
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Huang, Y.H., Sun, Y.T., Yang, Z., Lyu, X., Cao, Y.P., Qi, X.: Sc-gs: Sparse-controlled gaussian splatting for editable dynamic scenes. CVPR (2024)
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Luiten, J., Kopanas, G., Leibe, B., Ramanan, D.: Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis. In: 3DV (2024)
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Wu, G., Yi, T., Fang, J., Xie, L., Zhang, X., Wei, W., Liu, W., Tian, Q., Xinggang, W.: 4d gaussian splatting for real-time dynamic scene rendering. In: CVPR (2024)
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Xie, T., Zong, Z., Qiu, Y., Li, X., Feng, Y., Yang, Y., Jiang, C.: Physgaussian: Physics-integrated 3d gaussians for generative dynamics. In: CVPR (2024)
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Yang, Z., Yang, H., Pan, Z., Zhang, L.: Real-time photorealistic dynamic scene representation and rendering with 4d gaussian splatting. In: ICLR (2024)
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Yang, Z., Gao, X., Zhou, W., Jiao, S., Zhang, Y., Jin, X.: Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction. In: CVPR (2024)
2024
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