Fetching the paper…
Reading the bibliography…
This paper presents GGRt, a novel approach to generalizable novel view synthesis that alleviates the need for real camera poses, complexity in processing high-resolution images, and lengthy optimization processes, thus facilitating stronger applicability of 3D Gaussian Splatting (3D-GS) in real-world scenarios.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE TIP 13
2004
Earlier work this paper cites.
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite pp. 3354–3361 (2012)
2012
Earlier work this paper cites.
Yao, Y., Luo, Z., Li, S., Fang, T., Quan, L.: Mvsnet: Depth inference for unstructured multi-view stereo. In: ECCV. pp. 767–783 (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: CVPR. pp. 586–595 (2018)
2018
Earlier work this paper cites.
Godard, C., Mac Aodha, O., Firman, M., Brostow, G.J.: Digging into self-supervised monocular depth estimation. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 3828–3838 (2019)
2019
Earlier work this paper cites.
Mildenhall, B., Srinivasan, P.P., Ortiz-Cayon, R., Kalantari, N.K., Ramamoorthi, R., Ng, R., Kar, A.: Local light field fusion: Practical view synthesis with prescriptive sampling guidelines. ACM TOG 38
2019
Earlier work this paper cites.
Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., Vasudevan, V., Han, W., Ngiam, J., Zhao, H., Timofeev, A., Ettinger, S., Krivokon, M., Gao, A., Joshi, A., Zhang, Y., Shlens, J., Chen, Z., Anguelov, D.: Scalability in perception for autonomous driving: Waymo open dataset. In: CVPR (June 2020)
2020
Earlier work this paper cites.
Teed, Z., Deng, J.: Raft: Recurrent all-pairs field transforms for optical flow. In: ECCV. pp. 402–419. Springer (2020)
2020
Earlier work this paper cites.
Lai, Z., Liu, S., Efros, A.A., Wang, X.: Video autoencoder: self-supervised disentanglement of static 3d structure and motion. In: ICCV. pp. 9730–9740 (2021)
2021
Earlier work this paper cites.
Lin, C.H., Ma, W.C., Torralba, A., Lucey, S.: Barf: Bundle-adjusting neural radiance fields. In: ICCV. pp. 5741–5751 (2021)
2021
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. Communications of the ACM 65
2021
Earlier work this paper cites.
Sitzmann, V., Rezchikov, S., Freeman, B., Tenenbaum, J., Durand, F.: Light field networks: Neural scene representations with single-evaluation rendering. Advances in Neural Information Processing Systems 34
2021
Earlier work this paper cites.
Wang, Q., Wang, Z., Genova, K., Srinivasan, P.P., Zhou, H., Barron, J.T., Martin-Brualla, R., Snavely, N., Funkhouser, T.: IBRNet: Learning multi-view image-based rendering. In: CVPR. pp. 4690–4699 (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Yen-Chen, L., Florence, P., Barron, J.T., Rodriguez, A., Isola, P., Lin, T.Y.: iNeRF: Inverting neural radiance fields for pose estimation. In: IROS. pp. 1323–1330. IEEE (2021)
2021
Cited alongside, same era.
Yu, A., Ye, V., Tancik, M., Kanazawa, A.: pixelNeRF: Neural radiance fields from one or few images. In: CVPR. pp. 4578–4587 (2021)
2021
Cited alongside, same era.
Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM TOG 42
2023
Later among the works it cites.
Meuleman, A., Liu, Y.L., Gao, C., Huang, J.B., Kim, C., Kim, M.H., Kopf, J.: Progressively optimized local radiance fields for robust view synthesis. In: CVPR. pp. 16539–16548 (2023)
2023
Later among the works it cites.
Sajjadi, M.S., Mahendran, A., Kipf, T., Pot, E., Duckworth, D., Lučić, M., Greff, K.: Rust: Latent neural scene representations from unposed imagery. In: CVPR. pp. 17297–17306 (2023)
2023
Later among the works it cites.
Smith, C., Du, Y., Tewari, A., Sitzmann, V.: Flowcam: Training generalizable 3d radiance fields without camera poses via pixel-aligned scene flow. In: NeurIPS (2023)
2023
Later among the works it cites.
Tian, F., Du, S., Duan, Y.: Mononerf: Learning a generalizable dynamic radiance field from monocular videos. In: ICCV. pp. 17903–17913 (2023)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
Sajjadi, M.S.M., Meyer, H., Pot, E., Bergmann, U., Greff, K., Radwan, N., Vora, S., Lucic, M., Duckworth, D., Dosovitskiy, A., Uszkoreit, J., Funkhouser, T., Tagliasacchi, A.: Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations. In: CVPR (2022)
2022
Cited alongside, same era.
Suhail, M., Esteves, C., Sigal, L., Makadia, A.: Light field neural rendering. In: CVPR. pp. 8269–8279 (2022)
2022
Cited alongside, same era.
Zhang, K., Kolkin, N., Bi, S., Luan, F., Xu, Z., Shechtman, E., Snavely, N.: Arf: Artistic radiance fields. In: ECCV. pp. 717–733. Springer (2022)
2022
Cited alongside, same era.
Bian, W., Wang, Z., Li, K., Bian, J.W., Prisacariu, V.A.: Nope-nerf: Optimising neural radiance field with no pose prior. In: CVPR. pp. 4160–4169 (2023)
2023
Cited alongside, same era.
Chen, Y., Lee, G.H.: Dbarf: Deep bundle-adjusting generalizable neural radiance fields. In: CVPR. pp. 24–34 (2023)
2023
Cited alongside, same era.
Gu, X., Yuan, W., Dai, Z., Tang, C., Zhu, S., Tan, P.: DRO: Deep recurrent optimizer for video to depth. IEEE Robotics and Automation Letters 8
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
Wang, P., Chen, X., Chen, T., Venugopalan, S., Wang, Z., et al.: Is attention all nerf needs? In: ICLR (2023)
2023
Later among the works it cites.
Charatan, D., Li, S., Tagliasacchi, A., Sitzmann, V.: pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction. In: CVPR (2024)
2024
Closest in time.
Fu, Y., De Mello, S., Li, X., Kulkarni, A., Kautz, J., Wang, X., Liu, S.: 3d reconstruction with generalizable neural fields using scene priors. In: ICLR (2024)
2024
Closest in time.
Fu, Y., Liu, S., Kulkarni, A., Kautz, J., Efros, A.A., Wang, X.: Colmap-free 3d gaussian splatting. In: CVPR (2024)
2024
Closest in time.
Hong, Y., Zhang, K., Gu, J., Bi, S., Zhou, Y., Liu, D., Liu, F., Sunkavalli, K., Bui, T., Tan, H.: Lrm: Large reconstruction model for single image to 3d. In: ICLR (2024)
2024
Closest in time.
Li, J., Tan, H., Zhang, K., Xu, Z., Luan, F., Xu, Y., Hong, Y., Sunkavalli, K., Shakhnarovich, G., Bi, S.: Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model. In: ICLR (2024)
2024
Closest in time.
Wang, P., Tan, H., Bi, S., Xu, Y., Luan, F., Sunkavalli, K., Wang, W., Xu, Z., Zhang, K.: Pf-lrm: Pose-free large reconstruction model for joint pose and shape prediction. In: ICLR (2024)
2024
Closest in time.