Fetching the paper…
Reading the bibliography…
Neural fields have emerged as a new data representation paradigm and have shown remarkable success in various signal representations.
McCloskey, M., Cohen, N.J.: Catastrophic interference in connectionist networks: The sequential learning problem. In: Psychology of learning and motivation, vol. 24, pp. 109–165. Elsevier (1989)
1989
Earlier work this paper cites.
Gall, D.L.: MPEG: A video compression standard for multimedia applications. Commun. ACM 34
1991
Earlier work this paper cites.
Pennebaker, W.B., Mitchell, J.L.: JPEG: Still image data compression standard. Springer Science & Business Media (1992)
1992
Earlier work this paper cites.
Thrun, S.: A lifelong learning perspective for mobile robot control. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 23–30. IEEE (October 1994)
1994
Earlier work this paper cites.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13
2004
Earlier work this paper cites.
2013
Earlier work this paper cites.
2015
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: International Conference on Learning Representations (2015)
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
Hastie, T.J.: Generalized additive models. In: Statistical models in S, pp. 249–307. Routledge (2017)
2017
Earlier work this paper cites.
Li, H., Xu, Z., Taylor, G., Studer, C., Goldstein, T.: Visualizing the loss landscape of neural nets. In: Advances in Neural Information Processing Systems. vol. 31, pp. 6391–6401. Curran Associates, Inc. (2018)
2018
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 586–595 (June 2018)
2018
Earlier work this paper cites.
Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5939–5948 (June 2019)
2019
Earlier work this paper cites.
Genova, K., Cole, F., Vlasic, D., Sarna, A., Freeman, W.T., Funkhouser, T.: Learning shape templates with structured implicit functions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 7154–7164 (October 2019)
2019
Earlier work this paper cites.
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., Geiger, A.: Occupancy networks: Learning 3d reconstruction in function space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4460–4470 (June 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: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 165–174 (June 2019)
2019
Earlier work this paper cites.
Rahaman, N., Baratin, A., Arpit, D., Draxler, F., Lin, M., Hamprecht, F., Bengio, Y., Courville, A.: On the spectral bias of neural networks. In: Proceedings of the International Conference on Machine Learning (ICML). pp. 5301–5310. PMLR (June 2019)
2019
Earlier work this paper cites.
Wu, L., Wang, D., Liu, Q.: Splitting steepest descent for growing neural architectures. In: Advances in Neural Information Processing Systems. vol. 32, pp. 10655–10665. Curran Associates, Inc. (2019)
2019
Earlier work this paper cites.
Yu, J., Huang, T.S.: Universally slimmable networks and improved training techniques. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 1803–1811 (October 2019)
2019
Earlier work this paper cites.
Yu, J., Yang, L., Xu, N., Yang, J., Huang, T.: Slimmable neural networks. In: International Conference on Learning Representations (2019)
2019
Cited alongside, same era.
Chabra, R., Lenssen, J.E., Ilg, E., Schmidt, T., Straub, J., Lovegrove, S., Newcombe, R.: Deep local shapes: Learning local sdf priors for detailed 3d reconstruction. In: European Conference on Computer Vision. pp. 608–625. Springer (2020)
2020
Cited alongside, same era.
Erler, P., Guerrero, P., Ohrhallinger, S., Mitra, N.J., Wimmer, M.: Points2surf learning implicit surfaces from point clouds. In: European Conference on Computer Vision. pp. 108–124. Springer (2020)
2020
Cited alongside, same era.
Jiang, C.M., Sud, A., Makadia, A., Huang, J., Niessner, M., Funkhouser, T.: Local implicit grid representations for 3d scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6001–6010 (June 2020)
2020
Cited alongside, same era.
Martel, J.N.P., Lindell, D.B., Lin, C.Z., Chan, E.R., Monteiro, M., Wetzstein, G.: Acorn: adaptive coordinate networks for neural scene representation. ACM Trans. Graph. 40
2021
Later among the works it cites.
Martin-Brualla, R., Radwan, N., Sajjadi, M.S.M., Barron, J.T., Dosovitskiy, A., Duckworth, D.: Nerf in the wild: Neural radiance fields for unconstrained photo collections. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 7210–7219 (June 2021)
2021
Later among the works it cites.
Mehta, I., Gharbi, M., Barnes, C., Shechtman, E., Ramamoorthi, R., Chandraker, M.: Modulated periodic activations for generalizable local functional representations. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 14214–14223 (October 2021)
2021
Later among the works it cites.
Niemeyer, M., Geiger, A.: Giraffe: Representing scenes as compositional generative neural feature fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11453–11464 (June 2021)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mercat, A., Viitanen, M., Vanne, J.: UVG dataset: 50/120fps 4k sequences for video codec analysis and development. In: Proceedings of the 11th ACM Multimedia Systems Conference, MMSys. pp. 297–302. ACM (June 2020)
2020
Cited alongside, same era.
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: European Conference on Computer Vision. pp. 405–421. Springer (2020)
2020
Cited alongside, same era.
Schwarz, K., Liao, Y., Niemeyer, M., Geiger, A.: Graf: Generative radiance fields for 3d-aware image synthesis. In: Advances in Neural Information Processing Systems. vol. 33, pp. 20154–20166. Curran Associates, Inc. (2020)
2020
Cited alongside, same era.
Sitzmann, V., Chan, E., Tucker, R., Snavely, N., Wetzstein, G.: Metasdf: Meta-learning signed distance functions. In: Advances in Neural Information Processing Systems. vol. 33, pp. 10136–10147. Curran Associates, Inc. (2020)
2020
Cited alongside, same era.
Sitzmann, V., Martel, J., Bergman, A., Lindell, D., Wetzstein, G.: Implicit neural representations with periodic activation functions. In: Advances in Neural Information Processing Systems. vol. 33, pp. 7462–7473. Curran Associates, Inc. (2020)
2020
Cited alongside, same era.
Tancik, M., Srinivasan, P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J., Ng, R.: Fourier features let networks learn high frequency functions in low dimensional domains. In: Advances in Neural Information Processing Systems. vol. 33, pp. 7537–7547. Curran Associates, Inc. (2020)
2020
Cited alongside, same era.
Wu, L., Liu, B., Stone, P., Liu, Q.: Firefly neural architecture descent: a general approach for growing neural networks. In: Advances in Neural Information Processing Systems. vol. 33, pp. 22373–22383. Curran Associates, Inc. (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Later among the works it cites.
Reiser, C., Peng, S., Liao, Y., Geiger, A.: Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 14335–14345 (October 2021)
2021
Later among the works it cites.
Takikawa, T., Litalien, J., Yin, K., Kreis, K., Loop, C., Nowrouzezahrai, D., Jacobson, A., McGuire, M., Fidler, S.: Neural geometric level of detail: Real-time rendering with implicit 3d shapes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11358–11367 (June 2021)
2021
Later among the works it cites.
Yu, A., Li, R., Tancik, M., Li, H., Ng, R., Kanazawa, A.: Plenoctrees for real-time rendering of neural radiance fields. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 5752–5761 (October 2021)
2021
Later among the works it cites.
2022
Closest in time.
Evci, U., van Merrienboer, B., Unterthiner, T., Pedregosa, F., Vladymyrov, M.: Gradmax: Growing neural networks using gradient information. In: International Conference on Learning Representations (2022)
2022
Closest in time.
Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., Kanazawa, A.: Plenoxels: Radiance fields without neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5501–5510 (June 2022)
2022
Closest in time.
Landgraf, Z., Hornung, A.S., Cabral, R.S.: Pins: Progressive implicit networks for multi-scale neural representations. In: Proceedings of the International Conference on Machine Learning (ICML). pp. 11969–11984. PMLR (July 2022)
2022
Closest in time.
Lindell, D.B., Van Veen, D., Park, J.J., Wetzstein, G.: Bacon: Band-limited coordinate networks for multiscale scene representation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 16252–16262 (June 2022)
2022
Closest in time.
Müller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph. 41
2022
Closest in time.
2022
Closest in time.
Sun, C., Sun, M., Chen, H.T.: Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5459–5469 (June 2022)
2022
Closest in time.
2022
Closest in time.
Tancik, M., Casser, V., Yan, X., Pradhan, S., Mildenhall, B., Srinivasan, P.P., Barron, J.T., Kretzschmar, H.: Block-nerf: Scalable large scene neural view synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 8248–8258 (June 2022)
2022
Closest in time.
Xie, Y., Takikawa, T., Saito, S., Litany, O., Yan, S., Khan, N., Tombari, F., Tompkin, J., Sitzmann, V., Sridhar, S.: Neural fields in visual computing and beyond. Comput. Graph. Forum 41
2022
Closest in time.