Built on

Alliez, P., Cohen-Steiner, D., Tong, Y., Desbrun, M.: Voronoi-based variational reconstruction of unoriented point sets. In: Symposium on Geometry processing. vol. 7, pp. 39–48 (2007)
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Rahimi, A., Recht, B., et al.: Random features for large-scale kernel machines. In: NIPS. vol. 3, p. 5. Citeseer (2007)
2007
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Stanley, K.O.: Compositional pattern producing networks: A novel abstraction of development. Genetic programming and evolvable machines 8
2007
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Zhou, D., Burges, C.J.: High-order regularization on graphs. In: Proceedings of the 6th International Workshop on Mining and Learning with Graphs (2008)
2008
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Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 427–436 (2015)
2015
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Pang, J., Cheung, G.: Graph laplacian regularization for image denoising: Analysis in the continuous domain. IEEE Transactions on Image Processing 26
2017
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Chen, R.T., Rubanova, Y., Bettencourt, J., Duvenaud, D.: Neural ordinary differential equations. arXiv preprint arXiv:1806.07366 (2018)
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2018
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Dehghani, M., Gouws, S., Vinyals, O., Uszkoreit, J., Kaiser, Ł.: Universal transformers. arXiv preprint arXiv:1807.03819 (2018)
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2018
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Shaw, P., Uszkoreit, J., Vaswani, A.: Self-attention with relative position representations. arXiv preprint arXiv:1803.02155 (2018)
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2018
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Chen, Z., Zhang, H.: Learning implicit fields for generative shape modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5939–5948 (2019)
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Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942 (2019)
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2019
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Oechsle, M., Mescheder, L., Niemeyer, M., Strauss, T., Geiger, A.: Texture fields: Learning texture representations in function space. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4531–4540 (2019)
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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)
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Rahaman, N., Baratin, A., Arpit, D., Draxler, F., Lin, M., Hamprecht, F., Bengio, Y., Courville, A.: On the spectral bias of neural networks. In: International Conference on Machine Learning. pp. 5301–5310. PMLR (2019)
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Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., Li, H.: Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2304–2314 (2019)
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Sitzmann, V., Zollhöfer, M., Wetzstein, G.: Scene representation networks: Continuous 3d-structure-aware neural scene representations. arXiv preprint arXiv:1906.01618 (2019)
Original
2019
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Similar

Wang, B., Zhao, D., Lioma, C., Li, Q., Zhang, P., Simonsen, J.G.: Encoding word order in complex embeddings. arXiv preprint arXiv:1912.12333 (2019)
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2019
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Zhong, E.D., Bepler, T., Davis, J.H., Berger, B.: Reconstructing continuous distributions of 3d protein structure from cryo-em images. arXiv preprint arXiv:1909.05215 (2019)
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Deng, B., Lewis, J.P., Jeruzalski, T., Pons-Moll, G., Hinton, G., Norouzi, M., Tagliasacchi, A.: Nasa neural articulated shape approximation. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VII 16. pp. 612–628. Springer (2020)
2020

Then

Dupont, E., Goliński, A., Alizadeh, M., Teh, Y.W., Doucet, A.: Coin: Compression with implicit neural representations. arXiv preprint arXiv:2103.03123 (2021)
Original
2021
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Jain, A., Tancik, M., Abbeel, P.: Putting nerf on a diet: Semantically consistent few-shot view synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5885–5894 (2021)
2021
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Martin-Brualla, R., Radwan, N., Sajjadi, M.S., 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. pp. 7210–7219 (2021)
2021
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Mu, J., Qiu, W., Kortylewski, A., Yuille, A., Vasconcelos, N., Wang, X.: A-sdf: Learning disentangled signed distance functions for articulated shape representation. arXiv preprint arXiv:2104.07645 (2021)
Original
2021
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2021

Learning Positional Embeddings for Coordinate-MLPs

Ramasinghe, Sameera, Lucey, Simon

Understand

We propose a novel method to enhance the performance of coordinate-MLPs by learning instance-specific positional embeddings.

End-to-end optimization of positional embedding parameters along with network weights leads to poor generalization performance.
Instead, we develop a generic framework to learn the positional embedding based on the classic graph-Laplacian regularization, which can implicitly balance the trade-off between memorization and generalization.
This framework is then used to propose a novel positional embedding scheme, where the hyperparameters are learned per coordinate (i.e, instance) to deliver optimal performance.

Built on

Alliez, P., Cohen-Steiner, D., Tong, Y., Desbrun, M.: Voronoi-based variational reconstruction of unoriented point sets. In: Symposium on Geometry processing. vol. 7, pp. 39–48 (2007)
2007
Earlier work this paper cites.
Rahimi, A., Recht, B., et al.: Random features for large-scale kernel machines. In: NIPS. vol. 3, p. 5. Citeseer (2007)
2007
Earlier work this paper cites.
Stanley, K.O.: Compositional pattern producing networks: A novel abstraction of development. Genetic programming and evolvable machines 8
2007
Earlier work this paper cites.
Zhou, D., Burges, C.J.: High-order regularization on graphs. In: Proceedings of the 6th International Workshop on Mining and Learning with Graphs (2008)
2008
Earlier work this paper cites.
Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 427–436 (2015)
2015
Earlier work this paper cites.
Pang, J., Cheung, G.: Graph laplacian regularization for image denoising: Analysis in the continuous domain. IEEE Transactions on Image Processing 26
2017
Earlier work this paper cites.
Chen, R.T., Rubanova, Y., Bettencourt, J., Duvenaud, D.: Neural ordinary differential equations. arXiv preprint arXiv:1806.07366 (2018)
Original
2018
Earlier work this paper cites.
Dehghani, M., Gouws, S., Vinyals, O., Uszkoreit, J., Kaiser, Ł.: Universal transformers. arXiv preprint arXiv:1807.03819 (2018)
Original
2018
Earlier work this paper cites.
Shaw, P., Uszkoreit, J., Vaswani, A.: Self-attention with relative position representations. arXiv preprint arXiv:1803.02155 (2018)
Original
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. pp. 5939–5948 (2019)
2019
Earlier work this paper cites.
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942 (2019)
Original
2019
Earlier work this paper cites.
Oechsle, M., Mescheder, L., Niemeyer, M., Strauss, T., Geiger, A.: Texture fields: Learning texture representations in function space. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4531–4540 (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.
Rahaman, N., Baratin, A., Arpit, D., Draxler, F., Lin, M., Hamprecht, F., Bengio, Y., Courville, A.: On the spectral bias of neural networks. In: International Conference on Machine Learning. pp. 5301–5310. PMLR (2019)
2019
Earlier work this paper cites.
Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., Li, H.: Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2304–2314 (2019)
2019
Earlier work this paper cites.
Sitzmann, V., Zollhöfer, M., Wetzstein, G.: Scene representation networks: Continuous 3d-structure-aware neural scene representations. arXiv preprint arXiv:1906.01618 (2019)
Original
2019
Earlier work this paper cites.

Similar

Wang, B., Zhao, D., Lioma, C., Li, Q., Zhang, P., Simonsen, J.G.: Encoding word order in complex embeddings. arXiv preprint arXiv:1912.12333 (2019)
Original
2019
Cited alongside, same era.
Zhong, E.D., Bepler, T., Davis, J.H., Berger, B.: Reconstructing continuous distributions of 3d protein structure from cryo-em images. arXiv preprint arXiv:1909.05215 (2019)
Original
2019
Cited alongside, same era.
Bi, S., Xu, Z., Srinivasan, P., Mildenhall, B., Sunkavalli, K., Hašan, M., Hold-Geoffroy, Y., Kriegman, D., Ramamoorthi, R.: Neural reflectance fields for appearance acquisition. arXiv preprint arXiv:2008.03824 (2020)
Original
2020
Cited alongside, same era.
Deng, B., Lewis, J.P., Jeruzalski, T., Pons-Moll, G., Hinton, G., Norouzi, M., Tagliasacchi, A.: Nasa neural articulated shape approximation. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VII 16. pp. 612–628. Springer (2020)
2020

Then

Dupont, E., Goliński, A., Alizadeh, M., Teh, Y.W., Doucet, A.: Coin: Compression with implicit neural representations. arXiv preprint arXiv:2103.03123 (2021)
Original
2021
Closest in time.
Jain, A., Tancik, M., Abbeel, P.: Putting nerf on a diet: Semantically consistent few-shot view synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5885–5894 (2021)
2021
Closest in time.
Martin-Brualla, R., Radwan, N., Sajjadi, M.S., 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. pp. 7210–7219 (2021)
2021
Closest in time.
Mu, J., Qiu, W., Kortylewski, A., Yuille, A., Vasconcelos, N., Wang, X.: A-sdf: Learning disentangled signed distance functions for articulated shape representation. arXiv preprint arXiv:2104.07645 (2021)
Original
2021
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

Genova, K., Cole, F., Sud, A., Sarna, A., Funkhouser, T.: Local deep implicit functions for 3d shape. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4857–4866 (2020)

2020

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Guo, M., Fathi, A., Wu, J., Funkhouser, T.: Object-centric neural scene rendering. arXiv preprint arXiv:2012.08503 (2020)

Original

2020

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Henzler, P., Mitra, N.J., Ritschel, T.: Learning a neural 3d texture space from 2d exemplars. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8356–8364 (2020)

2020

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Liu, X., Yu, H.F., Dhillon, I., Hsieh, C.J.: Learning to encode position for transformer with continuous dynamical model. In: International Conference on Machine Learning. pp. 6327–6335. PMLR (2020)

2020

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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.

Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3504–3515 (2020)

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Sitzmann, V., Martel, J., Bergman, A., Lindell, D., Wetzstein, G.: Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems 33

2020

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Tancik, M., Srinivasan, P.P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J.T., Ng, R.: Fourier features let networks learn high frequency functions in low dimensional domains. arXiv preprint arXiv:2006.10739 (2020)

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2020

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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

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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

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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

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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. pp. 11358–11367 (2021)

2021

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Tiwari, G., Sarafianos, N., Tung, T., Pons-Moll, G.: Neural-gif: Neural generalized implicit functions for animating people in clothing. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 11708–11718 (2021)

2021

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Wang, Z., Wu, S., Xie, W., Chen, M., Prisacariu, V.A.: Nerf–: Neural radiance fields without known camera parameters. arXiv preprint arXiv:2102.07064 (2021)

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2021

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Xiang, F., Xu, Z., Hasan, M., Hold-Geoffroy, Y., Sunkavalli, K., Su, H.: Neutex: Neural texture mapping for volumetric neural rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7119–7128 (2021)

2021

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Xu, H., Alldieck, T., Sminchisescu, C.: H-nerf: Neural radiance fields for rendering and temporal reconstruction of humans in motion (2021)

2021

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Yu, A., Li, R., Tancik, M., Li, H., Ng, R., Kanazawa, A.: Plenoctrees for real-time rendering of neural radiance fields. arXiv preprint arXiv:2103.14024 (2021)

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Yu, A., Ye, V., Tancik, M., Kanazawa, A.: pixelnerf: Neural radiance fields from one or few images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4578–4587 (2021)

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Zheng, J., Ramasinghe, S., Lucey, S.: Rethinking positional encoding. arXiv preprint arXiv:2107.02561 (2021)

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