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The advent of 3D Gaussian Splatting (3DGS) has recently brought about a revolution in the field of neural rendering, facilitating high-quality renderings at real-time speed.
Ewa splatting
Zwicker, M., Pfister, H., Van Baar, J., and Gross, M · 2002
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Using multiple hypotheses to improve depth-maps for multi-view stereo
Campbell, N. D., Vogiatzis, G., Hernández, C., and Cipolla, R · 2008
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Patchmatch: A randomized correspondence algorithm for structural image editing
Barnes, C., Shechtman, E., Finkelstein, A., and Goldman, D. B · 2009
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Accurate, dense, and robust multiview stereopsis
Furukawa, Y. and Ponce, J · 2009
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Fast normalized cross-correlation
Yoo, J.-C. and Han, T. H · 2009
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Patchmatch stereo-stereo matching with slanted support windows
Bleyer, M., Rhemann, C., and Rother, C · 2011
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Multi-view stereo: A tutorial
Furukawa, Y., Hernández, C., et al · 2015
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Pixelwise view selection for unstructured multi-view stereo
Schönberger, J. L., Zheng, E., Frahm, J.-M., and Pollefeys, M · 2016
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Atlasnet: Multi-atlas non-linear deep networks for medical image segmentation
Vakalopoulou, M., Chassagnon, G., Bus, N., Marini, R., Zacharaki, E. I., Revel, M.-P., and Paragios, N · 2018
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Mvsnet: Depth inference for unstructured multi-view stereo
Yao, Y., Luo, Z., Li, S., Fang, T., and Quan, L · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
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Point-based multi-view stereo network
Chen, R., Han, S., Xu, J., and Su, H · 2019
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Multi-scale geometric consistency guided multi-view stereo
Xu, Q. and Tao, W · 2019
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nuscenes: A multimodal dataset for autonomous driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O · 2020
Earlier work this paper cites.
Neural sparse voxel fields
Liu, L., Gu, J., Zaw Lin, K., Chua, T.-S., and Theobalt, C · 2020
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Occlusion-aware depth estimation with adaptive normal constraints
Long, X., Liu, L., Theobalt, C., and Wang, W · 2020
Cited alongside, same era.
Nerf: Representing scenes as neural radiance fields for view synthesis
Mildenhall, B., Srinivasan, P., Tancik, M., Barron, J., Ramamoorthi, R., and Ng, R · 2020
Cited alongside, same era.
Scalability in perception for autonomous driving: Waymo open dataset
Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et al · 2020
Cited alongside, same era.
Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields
Barron, J. T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., and Srinivasan, P. P · 2021
Cited alongside, same era.
Multi-view depth estimation using epipolar spatio-temporal networks
Long, X., Liu, L., Li, W., Theobalt, C., and Wang, W · 2021
Cited alongside, same era.
Mip-nerf 360: Unbounded anti-aliased neural radiance fields
Neusg: Neural implicit surface reconstruction with 3d gaussian splatting guidance
Chen, H., Li, C., and Lee, G. H · 2023
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Uc-nerf: Neural radiance field for under-calibrated multi-view cameras
Cheng, K., Long, X., Yin, W., Wang, J., Wu, Z., Ma, Y., Wang, K., Chen, X., and Chen, X · 2023
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Lightgaussian: Unbounded 3d gaussian compression with 15x reduction and 200+ fps
Fan, Z., Wang, K., Wen, K., Zhu, Z., Xu, D., and Wang, Z · 2023
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Cvrecon: Rethinking 3d geometric feature learning for neural reconstruction
Feng, Z., Yang, L., Guo, P., and Li, B · 2023
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Gaussianshader: 3d gaussian splatting with shading functions for reflective surfaces
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Barron, J. T., Mildenhall, B., Verbin, D., Srinivasan, P. P., and Hedman, P · 2022
Cited alongside, same era.
Tensorf: Tensorial radiance fields
Chen, A., Xu, Z., Geiger, A., Yu, J., and Su, H · 2022
Cited alongside, same era.
Plenoxels: Radiance fields without neural networks
Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., and Kanazawa, A · 2022
Cited alongside, same era.
Sparseneus: Fast generalizable neural surface reconstruction from sparse views
Long, X., Lin, C., Wang, P., Komura, T., and Wang, W · 2022
Cited alongside, same era.
Multiview stereo with cascaded epipolar raft
Ma, Z., Teed, Z., and Deng, J · 2022
Cited alongside, same era.
Instant neural graphics primitives with a multiresolution hash encoding
Müller, T., Evans, A., Schied, C., and Keller, A · 2022
Cited alongside, same era.
Dreamfusion: Text-to-3d using 2d diffusion
Poole, B., Jain, A., Barron, J. T., and Mildenhall, B · 2022
Cited alongside, same era.
Jiang, Y., Tu, J., Liu, Y., Gao, X., Long, X., Wang, W., and Ma, Y · 2023
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3d gaussian splatting for real-time radiance field rendering
Kerbl, B., Kopanas, G., Leimkühler, T., and Drettakis, G · 2023
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Compact 3d gaussian representation for radiance field
Lee, J. C., Rho, D., Sun, X., Ko, J. H., and Park, E · 2023
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Scaffold-gs: Structured 3d gaussians for view-adaptive rendering
Lu, T., Yu, M., Xu, L., Xiangli, Y., Wang, L., Lin, D., and Dai, B · 2023
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Compact 3d scene representation via self-organizing gaussian grids
Morgenstern, W., Barthel, F., Hilsmann, A., and Eisert, P · 2023
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Compact3d: Compressing gaussian splat radiance field models with vector quantization
Navaneet, K., Meibodi, K. P., Koohpayegani, S. A., and Pirsiavash, H · 2023
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Dreamgaussian: Generative gaussian splatting for efficient 3d content creation
Tang, J., Ren, J., Zhou, H., Liu, Z., and Zeng, G · 2023
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F2-nerf: Fast neural radiance field training with free camera trajectories
Wang, P., Liu, Y., Chen, Z., Liu, L., Liu, Z., Komura, T., Theobalt, C., and Wang, W · 2023
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Multi-scale 3d gaussian splatting for anti-aliased rendering
Yan, Z., Low, W. F., Chen, Y., and Lee, G. H · 2023
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Unisim: A neural closed-loop sensor simulator
Yang, Z., Chen, Y., Wang, J., Manivasagam, S., Ma, W.-C., Yang, A. J., and Urtasun, R · 2023
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Mip-splatting: Alias-free 3d gaussian splatting
Yu, Z., Chen, A., Huang, B., Sattler, T., and Geiger, A · 2023
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