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Neural Radiance Fields (NeRFs) have demonstrated the remarkable potential of neural networks to capture the intricacies of 3D objects.
A generalization of algebraic surface drawing
Blinn, J. F · 1982
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Ray tracing volume densities
Kajiya, J. T. and Von Herzen, B. P · 1984
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Splatting with shadows
Nulkar, M. and Mueller, K · 2001
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Generating and real-time rendering of clouds
Man, P · 2006
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3d-r2n2: A unified approach for single and multi-view 3d object reconstruction
Choy, C. B., Xu, D., Gwak, J., Chen, K., and Savarese, S · 2016
Earlier work this paper cites.
Accelerated generative models for 3d point cloud data
Eckart, B., Kim, K., Troccoli, A., Kelly, A., and Kautz, J · 2016
Earlier work this paper cites.
Deep learning 3d shape surfaces using geometry images
Sinha, A., Bai, J., and Ramani, K · 2016
Earlier work this paper cites.
Synthesizing 3d shapes via modeling multi-view depth maps and silhouettes with deep generative networks
Arsalan Soltani, A., Huang, H., Wu, J., Kulkarni, T. D., and Tenenbaum, J. B · 2017
Earlier work this paper cites.
Hierarchical surface prediction for 3d object reconstruction
Häne, C., Tulsiani, S., and Malik, J · 2017
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Tanks and temples: Benchmarking large-scale scene reconstruction
Knapitsch, A., Park, J., Zhou, Q.-Y., and Koltun, V · 2017
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Neural sparse voxel fields
Liu, L., Gu, J., Zaw Lin, K., Chua, T.-S., and Theobalt, C · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., and Ng, R · 2020
Earlier work this paper cites.
Hypernetwork approach to generating point clouds
Spurek, P., Winczowski, S., Tabor, J., Zamorski, M., Zieba, M., and Trzciński, T · 2020
Earlier work this paper cites.
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.
Neural rgb-d surface reconstruction
Azinović, D., Martin-Brualla, R., Goldman, D. B., Nießner, M., and Thies, J · 2022
Cited alongside, same era.
Mip-nerf 360: Unbounded anti-aliased neural radiance fields
Barron, J. T., Mildenhall, B., Verbin, D., Srinivasan, P. P., and Hedman, P · 2022
Cited alongside, same era.
Efficient geometry-aware 3d generative adversarial networks
Chan, E. R., Lin, C. Z., Chan, M. A., Nagano, K., Pan, B., De Mello, S., Gallo, O., Guibas, L. J., Tremblay, J., Khamis, S., et al · 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.
Depth-supervised nerf: Fewer views and faster training for free
Deng, K., Liu, A., Zhu, J.-Y., and Ramanan, D · 2022
Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction
Sun, C., Sun, M., and Chen, H.-T · 2022
Later among the works it cites.
Block-nerf: Scalable large scene neural view synthesis
Tancik, M., Casser, V., Yan, X., Pradhan, S., Mildenhall, B., Srinivasan, P. P., Barron, J. T., and Kretzschmar, H · 2022
Later among the works it cites.
Ref-nerf: Structured view-dependent appearance for neural radiance fields
Verbin, D., Hedman, P., Mildenhall, B., Zickler, T., Barron, J. T., and Srinivasan, P. P · 2022
Later among the works it cites.
Continuous conditional random field convolution for point cloud segmentation
Yang, F., Davoine, F., Wang, H., and Jin, Z · 2022
Later among the works it cites.
Single-stage diffusion nerf: A unified approach to 3d generation and reconstruction
Chen, H., Gu, J., Chen, A., Tian, W., Tu, Z., Liu, L., and Su, H · 2023
Closest in time.
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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.
Shape, light, and material decomposition from images using monte carlo rendering and denoising
Hasselgren, J., Hofmann, N., and Munkberg, 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.
Extracting triangular 3d models, materials, and lighting from images
Munkberg, J., Hasselgren, J., Shen, T., Gao, J., Chen, W., Evans, A., Müller, T., and Fidler, S · 2022
Cited alongside, same era.
Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs
Niemeyer, M., Barron, J. T., Mildenhall, B., Sajjadi, M. S., Geiger, A., and Radwan, N · 2022
Cited alongside, same era.
Dense depth priors for neural radiance fields from sparse input views
Roessle, B., Barron, J. T., Mildenhall, B., Srinivasan, P. P., and Nießner, M · 2022
Cited alongside, same era.
Jiang, Y., Tu, J., Liu, Y., Gao, X., Long, X., Wang, W., and Ma, Y · 2023
Closest in time.
3d gaussian splatting for real-time radiance field rendering
Kerbl, B., Kopanas, G., Leimkühler, T., and Drettakis, G · 2023
Closest in time.
Flexible techniques for differentiable rendering with 3d gaussians
Keselman, L. and Hebert, M · 2023
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Envidr: Implicit differentiable renderer with neural environment lighting
Liang, R., Chen, H., Li, C., Chen, F., Panneer, S., and Vijaykumar, N · 2023
Closest in time.
Nero: Neural geometry and brdf reconstruction of reflective objects from multiview images
Liu, Y., Wang, P., Lin, C., Long, X., Wang, J., Liu, L., Komura, T., and Wang, W · 2023
Closest in time.
4d gaussian splatting for real-time dynamic scene rendering
Wu, G., Yi, T., Fang, J., Xie, L., Zhang, X., Wei, W., Liu, W., Tian, Q., and Wang, X · 2023
Closest in time.
Multiplanenerf: Neural radiance field with non-trainable representation
Zimny, D., Tabor, J., Zięba, M., and Spurek, P · 2023
Closest in time.