Fetching the paper…
Reading the bibliography…
The neural implicit representation has shown its effectiveness in novel view synthesis and high-quality 3D reconstruction from multi-view images.
Kajiya, J.T., Von Herzen, B.P.: Ray tracing volume densities. ACM SIGGRAPH computer graphics 18
1984
Earlier work this paper cites.
Max, N.: Optical models for direct volume rendering. IEEE Transactions on Visualization and Computer Graphics 1
1995
Earlier work this paper cites.
Kazhdan, M., Bolitho, M., Hoppe, H.: Poisson surface reconstruction. In: Proceedings of the fourth Eurographics symposium on Geometry processing. vol. 7 (2006)
2006
Earlier work this paper cites.
Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4104–4113 (2016)
2016
Earlier work this paper cites.
Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: ScanNet: Richly-annotated 3d reconstructions of indoor scenes. In: CVPR (2017)
2017
Earlier work this paper cites.
McCormac, J., Handa, A., Davison, A., Leutenegger, S.: Semanticfusion: Dense 3d semantic mapping with convolutional neural networks. In: 2017 IEEE International Conference on Robotics and automation (ICRA). pp. 4628–4635. IEEE (2017)
2017
Earlier work this paper cites.
Hassan, M., Choutas, V., Tzionas, D., Black, M.J.: Resolving 3d human pose ambiguities with 3d scene constraints. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 2282–2292 (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. pp. 4460–4470 (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. pp. 165–174 (2019)
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Xu, Q., Tao, W.: Multi-scale geometric consistency guided multi-view stereo. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5483–5492 (2019)
2019
Earlier work this paper cites.
Atzmon, M., Lipman, Y.: Sal: Sign agnostic learning of shapes from raw data. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Kohli, A., Sitzmann, V., Wetzstein, G.: Semantic Implicit Neural Scene Representations with Semi-supervised Training. In: International Conference on 3D Vision (3DV) (2020)
2020
Earlier work this paper cites.
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.
Nguyen-Phuoc, T.H., Richardt, C., Mai, L., Yang, Y., Mitra, N.: Blockgan: Learning 3d object-aware scene representations from unlabelled images. Advances in Neural Information Processing Systems 33
2020
Cited alongside, same era.
Nie, Y., Han, X., Guo, S., Zheng, Y., Chang, J., Zhang, J.J.: Total3dunderstanding: Joint layout, object pose and mesh reconstruction for indoor scenes from a single image. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 55–64 (2020)
2020
Park, K., Sinha, U., Barron, J.T., Bouaziz, S., Goldman, D.B., Seitz, S.M., Martin-Brualla, R.: Nerfies: Deformable neural radiance fields. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5865–5874 (2021)
2021
Later among the works it cites.
Pumarola, A., Corona, E., Pons-Moll, G., Moreno-Noguer, F.: D-nerf: Neural radiance fields for dynamic scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10318–10327 (2021)
2021
Later among the works it cites.
Rebain, D., Jiang, W., Yazdani, S., Li, K., Yi, K.M., Tagliasacchi, A.: Derf: Decomposed radiance fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14153–14161 (2021)
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. pp. 14335–14345 (2021)
2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Prajwal, K., Mukhopadhyay, R., Namboodiri, V.P., Jawahar, C.: A lip sync expert is all you need for speech to lip generation in the wild. In: Proceedings of the 28th ACM International Conference on Multimedia. pp. 484–492 (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Yariv, L., Kasten, Y., Moran, D., Galun, M., Atzmon, M., Ronen, B., Lipman, Y.: Multiview neural surface reconstruction by disentangling geometry and appearance. Advances in Neural Information Processing Systems 33
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Li, K., Rezatofighi, H., Reid, I.: Moltr: Multiple object localization, tracking and reconstruction from monocular rgb videos. IEEE Robotics and Automation Letters 6
2021
Cited alongside, same era.
Luan, F., Zhao, S., Bala, K., Dong, Z.: Unified shape and svbrdf recovery using differentiable monte carlo rendering: Supplemental material (2021)
2021
Cited alongside, same era.
Later among the works it cites.
Wang, P., Liu, L., Liu, Y., Theobalt, C., Komura, T., Wang, W.: Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. NeurIPS (2021)
2021
Later among the works it cites.
Yang, B., Zhang, Y., Xu, Y., Li, Y., Zhou, H., Bao, H., Zhang, G., Cui, Z.: Learning object-compositional neural radiance field for editable scene rendering. In: International Conference on Computer Vision (ICCV) (October 2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Zhang, X., Srinivasan, P.P., Deng, B., Debevec, P., Freeman, W.T., Barron, J.T.: Nerfactor: Neural factorization of shape and reflectance under an unknown illumination. ACM Transactions on Graphics (TOG) 40
2021
Later among the works it cites.
Zhi, S., Laidlow, T., Leutenegger, S., Davison, A.: In-place scene labelling and understanding with implicit scene representation. In: Proceedings of the International Conference on Computer Vision (ICCV) (2021)
2021
Later among the works it cites.
2022
Closest in time.
Guo, H., Peng, S., Lin, H., Wang, Q., Zhang, G., Bao, H., Zhou, X.: Neural 3d scene reconstruction with the manhattan-world assumption. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5511–5520 (2022)
2022
Closest in time.
2022
Closest in time.
Verbin, D., Hedman, P., Mildenhall, B., Zickler, T., Barron, J.T., Srinivasan, P.P.: Ref-NeRF: Structured view-dependent appearance for neural radiance fields. CVPR (2022)
2022
Closest in time.
Yu, H.X., Guibas, L., Wu, J.: Unsupervised discovery of object radiance fields. In: International Conference on Learning Representations (2022), https://openreview.net/forum?id=rwE8SshAlxw
2022
Closest in time.
Zhang, K., Luan, F., Li, Z., Snavely, N.: Iron: Inverse rendering by optimizing neural sdfs and materials from photometric images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5565–5574 (2022)
2022
Closest in time.